Download - Phd Dissertation Horvath
Empirical Essays on Monetary Economics
Roman Horváth
Doctoral Dissertation
2008
Institute of Economic Studies Faculty of Social Sciences, Charles University, Prague
Acknowledgments To my family; to my son David.
Contents - 3 -
Contents Introduction ................................................................................................................................ 4 1 The Time-Varying Policy Neutral Rate in Real Time: A Predictor for Future Inflation?............ 6
1.1 Introduction .................................................................................................................. 6 2 Related Literature ............................................................................................................. 9 2.1 Methodological Background .......................................................................................... 9 2.2 Methods for Natural Rate of Interest Estimation ......................................................... 11 3 Data and Empirical Methodology ................................................................................... 13 3.1 Monetary Policy Rules ................................................................................................. 13 3.2 Data............................................................................................................................. 19 4 Results ............................................................................................................................ 21 4.1 Time-varying Equilibrium Interest Rates...................................................................... 21 4.2 Monetary Policy Stance and Inflation Developments ................................................... 25 5 Conclusions .................................................................................................................... 31 References ......................................................................................................................... 33
2 Price Setting and Market Structure: An Empirical Analysis of Micro Data ............................. 43 2.1 Introduction ................................................................................................................ 43 2.2 Price Setting Behavior.................................................................................................. 44 2.3 Empirical analysis ........................................................................................................ 46 2.4 Conclusions ................................................................................................................. 62
3 Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy............. 80 3.1 Introduction ................................................................................................................ 80 3.2 Estimating inflation persistence ................................................................................... 84 3.3 Data............................................................................................................................. 88 3.4 Results ......................................................................................................................... 90 3.5 Conclusions ............................................................................................................... 103
4 The Effects of Monetary Policy in the Czech Republic: An Empirical Study ....................... 126 4.1 Introduction .............................................................................................................. 126 4.2 Related VAR Literature.............................................................................................. 128 4.3 Data........................................................................................................................... 133 4.4 Identification ............................................................................................................. 134 4.5 Results ....................................................................................................................... 137 4.6 Concluding Remarks.................................................................................................. 142 References ....................................................................................................................... 144
Introduction - 4 -
Introduction This dissertation contains four empirical essays on monetary economics. Each of them is self-
contained and independent of the others. Nevertheless, they all deal with various aspects of
monetary issues in the Central Europe and may be of interest outside of our region, too. More
specifically, all chapters look at the nexus of interest rates and prices and examine the impact of
interest rates on price developments or the behavior of prices itself.
Chapter 1 focuses on estimation of time-varying neutral interest rate. It applies a structural time-
varying parameter model with endogenous regressors to estimate a monetary policy rule in the
Czech Republic. Literature pointed out that the estimates of neutral interest rates are uncertain
and thus difficult to apply in daily policy work. I provide a counter-argument to this literature
showing that my estimates of neutral interest rates are able to explain future inflation
developments. The chapter is forthcoming in Economic Modelling.
Chapter 2 explores price setting behavior in Slovakia using a unique micro-level dataset about 5
millions observations. I argue that the effect of market structure on persistence of inflation
results from two conflicting forces. Increased competition may reduce persistence by increasing
the frequency of price changes. In contrast, higher competition may increase persistence through
inertial behaviour induced by the strategic complementarity among price setters. In this case
study, I find that the latter effects dominate. Chapter 2 is a joint work with Fabrizio Coricelli and
has been published as a discussion paper at the Centre for Economic Policy Research and it is
forthcoming in a special issue on price setting in the EU in Managerial and Decision Economics.
Chapter 3 studies inflation dynamics at the microeconomic level and adds further evidence in
support to the findings in Chapter 2. It discusses issues such as aggregation bias, the link between
the adoption of inflation target and inflation persistence and addresses services inflation
persistence puzzle. Chapter 3 is a joint work with Ian Babetskii and Fabrizio Coricelli and has
been published as a working paper at the Czech National Bank and CERGE-EI.
Chapter 4 analyzes the monetary policy effects within vector autoregression framework. It
documents a well-functioning monetary transmission mechanism in the Czech Republic similar
to the euro area countries, especially in terms of persistence of monetary policy shocks. Chapter 4
Introduction - 5 -
is a joint work with Magdalena Morgese Borys and working paper version is available at Czech
National Bank, CERGE-EI or at our institution. Currently, it is second revise-resubmit at
Empirica.
During writing this thesis, I benefited from numerous comments from my co-authors, colleagues
at the Czech National Bank and Charles University as well as the participants at various seminars
and conferences. I wish to thank to all of them.
Time-Varying Policy Neutral Rate - 6 -
1 The Time-Varying Policy Neutral Rate in Real Time: A Predictor for Future Inflation? *
1.1 Introduction Inflation targeting regimes are increasingly popular around the world. For example, regarding
Central and Eastern Europe, while the first two countries adopted explicit inflation targeting
regime back in 1998, there were already seven countries conducting inflation targeting in 2006
and others were contemplating doing so (International Monetary Fund, 2006).1 A characteristic
feature of inflation targeting is that central banks set the short-term nominal interest rate (path)
so as to get inflation and output to their targeted levels. When inflation and output is at their
targeted level, actual interest rate is equal to the equilibrium rate, which we label as the policy
neutral rate hereinafter.
In this regard, Woodford (2003) notes that central banks should on average track the policy
neutral rate to stabilize the economy. In a similar fashion, Taylor (1999) emphasizes that the
measurement of the policy neutral rate is one of the key issues for countries targeting inflation. In
this respect, it is of great importance for central banks to identify as precisely as possible the
policy neutral rate. This is quite an intricate exercise, as the policy neutral rate is unobservable;
however its mis-measurement is high-priced, as it may result in over- or undershooting the
inflation target.2
* I thank an anonymous referee, Juraj Antal, Ian Babetskii, Péter Benczúr, Martin Cincibuch, Woon Gyu Choi, Dana Hájková, Tomáš Holub, Anca Podpiera, Michal Ježek, Pavol Prievozník, Michal Skořepa, Guntram Wolff and the seminar participants at the Econometric Society - European Meeting, 11th International Conference on Macroeconomic Theory and Policy (Greece), DIW (Berlin), Czech National Bank, Czech Economic Society and Charles University (Prague) for helpful comments. All remaining errors are entirely my own. I am grateful to the Czech Economic Society for supporting this research. 1 The Czech Republic and Poland adopted inflation targeting in 1998, followed by Hungary in 2001, Romania and Slovakia in 2005 and Armenia and the Serbia in 2006 (note this is an updated list of Table 1 in IMF, 2006). Ukraine is likely to adopt inflation targeting in near future (IMF, 2006). 2 In case that trend productivity growth is positively correlated with policy neutral rate Trehan and Wu (2007) show that when the central bank mis-measures the trend productivity growth, it is in consequence likely to mis-measure policy neutral rate, too. But these two policy errors will tend to offset each other. Policy would be loose, when
Time-Varying Policy Neutral Rate - 7 -
In this light, it is quite striking that remarkably little evidence is available for Central and Eastern
European Countries (CEECs) on the estimation of the policy neutral rate. While there are dozens
of studies on equilibrium exchange rates in the new EU members, there is surprisingly very little
evidence on equilibrium interest rates (Brzoza-Brzezina, 2006, seems to be the only exception, with
evidence on Poland). This imbalance is rather paradoxical, as half the new EU members target
inflation (Czech Republic, Hungary, Poland, Romania and Slovakia), for which the concept of the
policy neutral rate is of primary importance for the conduct of monetary policy.3 Consequently,
this paper tries to bridge this gap.
This paper addresses the issue of policy neutral rate estimation in one of the new EU member
states, the Czech Republic, based on various specifications of simple Taylor-type monetary policy
rules. This former transition country provides an interesting case to evaluate the policy neutral
interest rate, as one can expect a certain pattern in the path of nominal and real equilibrium
interest rates over the longer term (note that the policy neutral rate is in fact the short-term
nominal equilibrium interest rate; more on definitions below).
Lipschitz et al. (2006) point out that at the outset of the transition the capital/labor ratios were
much lower than those in Western Europe and therefore the marginal product of capital and for
that reason the real equilibrium interest rate were rather high. Given the capital accumulation
over the course of the transition, there should be a tendency for the real equilibrium interest rate
to decrease. From an open economy perspective, the new EU members exhibited falling
exchange rate risk premium during their transition process to a market economy (Beneš and
central bank fails to recognize, that trend grows has slowed down and at the same time, policy would be restrictive, as the policy neutral rate has fallen. As a result, mismeasurement of the equilibrium real interest rate due to mismeasurement of productivity growth is likely to have little net impact on monetary policy. 3 See Coats et al. (2003) and Kotlan and Navratil (2003) on Czech monetary policy.
Time-Varying Policy Neutral Rate - 8 -
N’Diaye, 2004), which also puts downward pressure on real equilibrium interest rates (Archibald
and Hunter, 2001). Analogously, it is a well-documented empirical regularity that these countries
exhibit real equilibrium exchange rate appreciation (see Égert et al., 2006 for a comprehensive
survey of the sources of appreciation). A decrease in the foreign equilibrium interest rate, which
is reported by several authors for the euro area (e.g. Wintr et al., 2005), may, especially in a small
open economy, reduce the level of the domestic equilibrium interest rate as well. Additionally, the
path of nominal equilibrium interest rates should reflect not only the decrease of real equilibrium
rates, but also successful disinflation in transition countries (see Korhonen and Wachtel, 2006).
All in all, the aforementioned arguments provide a rationale for modeling the policy neutral rate
as time-varying.
In this paper we provide first an estimation of monetary policy rules with a time-varying intercept
to assess the fluctuations of the policy neutral interest rate over time. The novelty of our
approach lies in estimation of the policy neutral rate by the time-varying parameter model with
endogenous regressors (Kim, 2006).4 Unlike the ‘conventional’ time-varying parameter model5,
this approach is robust to endogeneity of the explanatory variables, which is indeed relevant
when estimating the monetary policy rules. While endogeneity is almost always accounted for in
the literature on time-invariant monetary policy rules, as it is estimated by the generalized method
of moments (GMM), it is almost never addressed in studies on time-varying policy rules. An
additional feature of this paper is that we utilize ex-post as well as real-time based data (see e.g.
Orphanides, 2001, on real-time data analysis within a monetary policy rules framework),
specifically the real-time output gap and real-time inflation forecast of the Czech National Bank
(CNB), to estimate the monetary policy rules.
4 Note that in the working paper version of Kim (2006), this procedure is also labeled as the augmented Kalman filter. 5 We label the time-varying parameter as conventional, when it does not account for endogeneity in the regressors hereinafter.
Time-Varying Policy Neutral Rate - 9 -
One of our primary policy applications, except for measuring the policy neutral rate by a novel
technique, is also to propose a measure of the monetary policy stance based on the difference
between the actual interest rate and the estimated policy neutral rate. Anticipating our results, we
find this measure of the monetary policy stance quite useful in predicting both the level and
change of the future inflation rate.
The paper is organized as follows. Section 2 discusses related literature. Section 3 describes our
data and empirical methodology. Section 4 gives the results of the estimation of time-varying
estimates of the policy neutral rate as well as an analysis of the ability of the monetary policy
stance to predict future inflation developments. Section 5 concludes. An appendix with additional
results follows.
2 Related Literature
2.1 Methodological Background It has been acknowledged in monetary economics for a long time that there exists some
unobservable real interest rate that equilibrates aggregate demand and aggregate supply
(Woodford, 2003). When the actual real interest rate is equal to the unobservable one, price
stability is achieved. This unobservable rate is often labeled as the natural rate of interest or
equilibrium interest rate. Equivalently, it has been noted that the equilibrium interest rate is the
real interest rate that prevails when prices are fully flexible in all markets (Neiss and Nelson,
2003; Woodford, 2003).
Consequently, the equilibrium interest rate or natural rate of interest is a fairly general concept
and in principle it may be well associated with short-term, medium-term or long-term interest
rates. In this context, it is worth pointing out that the determinants of the equilibrium interest
rate are likely to differ according to time horizon (different frequency movements). In the long-
Time-Varying Policy Neutral Rate - 10 -
term, the level of the equilibrium interest rate is influenced by supply-side structural
characteristics of economy such as long-run growth potential, which in turn depends on
technological progress, population growth and inter-temporal substitution of consumption
(Crespo-Cuaresma et al., 2005). In the medium-term, the equilibrium interest rate is associated
with business cycle fluctuations. In the short-term, the equilibrium interest rate is linked mainly
to demand factors related to monetary policy and its targeting horizon (Archibald and Hunter,
2001). Here monetary policy may systematically influence inflation expectations and in turn the
level of the short-term nominal equilibrium rate.
For the purposes of monetary policy conduct, it is vital to know which interest rate level the
monetary authority should set in order to achieve price stability (i.e. neutral policy stance). As the
primary monetary policy instrument is the level of the short-term interest rate, the equilibrium
interest rate in this context is a rather short-term concept and is often labeled as the policy
neutral rate (Coats et al., 2003; Lam and Tkacz, 2004; Beneš et al., 2005). The policy neutral rate
thus represents the nominal equilibrium interest rate and is defined as the real equilibrium interest
rate plus expected inflation (Coats et al., 2003). In other words, the policy neutral rate is linked to
the short-term nominal interest rate, over which the central bank has substantial control, and thus
the policy neutral rate may be understood as a rather narrower concept in comparison to
equilibrium interest rate and natural rate of interest.6
Should interest rate policy of the monetary authority strictly follow the neutral rate when
targeting inflation? Not necessarily. The first point is that obviously there is uncertainty in policy
neutral rate measurement. Second, and more importantly, there are shocks to which it is sub-
optimal for the authority to react. More specifically, central banks may wish deliberately not to
6 For convenience, we use policy neutral rate, natural rate of interest and equilibrium interest rate in the following text interchangeably to a certain extent. However, when we want to emphasize the short-run concept of it, we always use the term policy neutral rate.
Time-Varying Policy Neutral Rate - 11 -
react to the first-round effects of cost-push shocks such as those stemming from value added tax
changes, as this can be destabilizing to the economy in the short run. This may, however, alter
the inflation expectations of economic agents, if some fraction of them are myopic, and as a
result, induce a change in the policy neutral rate. In such case, the central bank’s interest rate
policy may temporarily deviate from the policy neutral rate.
2.2 Methods for Natural Rate of Interest Estimation Generally, there are several main methods for estimating the natural rate of interest (see e.g.
Giammarioli and Valla, 2004, for a survey).7 The simplest is to assume that the equilibrium is
captured reasonably well by some univariate trend such as an HP filter. Nevertheless, a number
of papers document that the estimates based on these filters are often misleading (Clark and
Kozicki, 2005). In general, the limitations of the univariate methods have been pointed out by
many authors (e.g. Canova, 1998).
Another method for deriving equilibrium interest rates is based on the estimation of a simple
monetary policy rule of the central bank (Taylor, 1993). The reaction function typically associates
short-term interest rate with its lagged value, the difference between inflation (forecast) and its
target, and the output gap. The intercept of the estimated reaction function can be interpreted as
the nominal equilibrium interest rate (that is, the interest rate that would prevail when inflation
and output are at their targeted values). This method has been applied to estimate the equilibrium
interest rates by e.g. Clarida et al. (1998, 2000) and Orphanides (2001) for the United States and
Germany, Adam et al. (2005) for the United Kingdom, and Gerdesmeier and Roffia (2004, 2005)
for the euro area. Nevertheless, the assumption of constant equilibrium interest rates is often
7 Note that we do not present the exhaustive list of methods for equilibrium interest rate estimation, for example Brzoza-Brzezina (2006) proposes a structural vector autoregression model in this regard. In general, the role of the equilibrium interest rate for monetary policy conduct is discussed extensively by Taylor (1993), Woodford (2003) and Amato (2005).
Time-Varying Policy Neutral Rate - 12 -
found too restrictive over the longer term. Consequently, a number of studies model the
equilibrium interest rate, or more generally the monetary policy rule as time-varying (see Plantier
and Scrimgeour, 2002, Elkhoury, 2006, and Kim and Nelson, 2006). Typically, these studies find
a rationale for modeling the rule as time-varying, given that the equilibrium interest rate
sometimes fluctuates considerably over longer time horizons (as do other parameters in the
policy rule). Generally, the monetary policy rules approach measures the behavior of the central
bank and assumes that the central bank estimates equilibrium interest rates correctly. In case of
systematic mis-measurement of equilibrium rates by the central bank, it is likely that the
equilibrium rates retrieved from the estimation of the reaction function are mis-measured as well.
Structural time series models represent another common method for measuring equilibrium
interest rates. The primary contribution in this area is Laubach and Williams (2003), who
formulate a simple empirical model containing an IS curve, a Phillips curve and an equation
linking the equilibrium interest rate to trend growth, and model equilibrium interest rates and
potential output as unobserved components. Their method has gained popularity recently and
has been applied by Manrique and Marques (2004) for the U.S. and Germany, Mesonnier and
Renne (2007) for the euro area, and Wintr, Guarda and Rouabah (2005) for the euro area8 and
Luxembourg as well. In principle, the joint estimation of equilibrium interest rates and the output
gap is an advantage of this approach; however it also reduces the degrees of freedom, which may
be an issue for transition countries with rather short time series.
Equilibrium interest rates can also be estimated within stochastic dynamic general equilibrium
models. The advantage of this type of literature is that it specifies the structure of the economy
and thus in principle allows identification of a variety of shocks hitting the economy. On the
other hand, Levin et al. (1999) find that more complex models seem to be less robust to model
8 See Crespo-Cuaresma et al. (2004) on related estimates for the euro area using a somewhat different methodology.
Time-Varying Policy Neutral Rate - 13 -
uncertainty (see also Giammarioli and Valla, 2004). Consequently, these model outcomes may be
quite sensitive to model assumptions. The recent examples of this approach to estimating
equilibrium interest rates include Giammarioli and Valla (2003), Neiss and Nelson (2003) and
Smets and Wouters (2003).
The last major stream of literature estimates equilibrium interest rates from the yield curve and
asset pricing models. Bomfim (2001) uses inflation linked bonds in order to eliminate the
distortions from inflation expectations and retrieves equilibrium interest rates from the realized
yields on U.S. Treasury inflation-indexed securities. In this regard, Giammarioli and Valla (2004)
discuss equilibrium interest rate estimates in relation to consumption capital asset pricing models.
Generally, this stream of literature hinges on the notion of liquid financial markets and thus this
approach is viable mainly for countries with developed financial markets.
3 Data and Empirical Methodology In this part, we discuss the methodology and dataset we employ to evaluate the policy neutral rate
fluctuations in the Czech Republic. Specifically, we estimate a variety of backward or forward
looking monetary policy rules with a time-varying policy neutral rate.
3.1 Monetary Policy Rules A starting point for a formal derivation of the monetary policy rule is the reasonable assumption
that the central bank targets to set the nominal interest rate in line with the state of the economy
(see Clarida et al., 1998, 2000), as postulated in Eq. (1):
( ) ttittitt xEErr Ω+−Ω+= ++ βππα *_
* (1)
Time-Varying Policy Neutral Rate - 14 -
where *tr denotes the targeted interest rate,
_r is the policy neutral rate, it+π stands for the
central bank forecast of the yearly inflation rate i periods ahead, and *it+π is the central bank’s
inflation target9. tx represents a measure of the output gap. (.)E is the expectation operator and
tΩ is the information set available at the time when interest rates are set. Hereinafter, we set i
either equal to 12 months to reflect the CNB’s actual targeting horizon10 or alternatively equal to
0, i.e. using the current inflation for the sensitivity analysis. Therefore, Eq. (1) links targeted
nominal interest rates to a constant (i.e. the interest rate – policy neutral rate – that would prevail
when expected inflation and output are at their targeted levels), the deviation of expected
inflation from the target and the output gap.
Nevertheless, Eq. (1) is often argued to be too restrictive, as it does not account for interest rate
smoothing of central banks. Clarida et al. (1998) assume that the central bank adjusts the interest
rate sluggishly to the targeted value. This is so for a number of reasons. For example, Goodfriend
(1991) puts forward concerns over the stability of financial markets. Sack (1997) highlights
uncertainty about the effects of interest rate changes on the economy.11 Instead of an explicit
listing of various factors behind the interest rate smoothing, Clarida et al. (1998) assume for
simplicity that the actual policy interest rate is a combination of its lagged value and the targeted
policy rate as in Eq. (2).
9 At the beginning of our sample period, the target has been defined as the band decreasing from 3%-5% to 2%-4% at the end of 2005. For this period, we take the mid-points of target. From 2006 onwards, the target is set in terms of yearly change of headline inflation of 3%. The evolution of target is depicted in Chart A.1 in the Appendix. 10 This is in line with the CNB main forecasting model – the Quarterly Prediction Model; see Coats et al., 2003. The actual targeting horizon is 12-18 months, but due to data limitations we prefer to work with 12 months. In general, see Batini and Nelson, 1999, for contributions on the optimal targeting horizon. Note also that the policy neutral rate is defined as the real rate plus the expected inflation in period t+k, where k is given by the maturity of the interbank rate (in our case k=3). k is thus different from the forecasting horizon i. As argued by Clarida et al. (2000), this is not very relevant in practice, as the short-term interbank interest rates at various maturities are strongly linked together. Indeed, the correlation of 3M PRIBOR and 12M PRIBOR – to reflect that i=12 – stands at 0.99 in our sample. 11 Nevertheless, Rudebusch (2006) recently questioned the extent of monetary policy inertia and argued that the inertia is rather low.
Time-Varying Policy Neutral Rate - 15 -
( ) tttt rrr νρρ +−+= −*
1 1 (2),12
where [ ]1,0∈ρ . In line with Clarida et al. (1998), substituting Eq. (2) into Eq. (1) and
eliminating unobserved forecast variables results in Eq. (3):
( ) ( ) tttititt rxrr ερβππαρ ++⎥⎦⎤
⎢⎣⎡ +−+−= −++ 1
*_
1 (3)
The disturbance term tε is a combination of forecast errors (i.e.,
[ ])()()1( ttttititt xExE Ω−+Ω−−−= ++ βππαρε ) and is thus orthogonal to all
information available at time t ( tΩ ).
Next, in order to estimate the time-varying neutral policy rate we apply a structural time-varying
coefficient model with endogenous regressors. Kim (2006) shows that the conventional time-
varying parameter model delivers inconsistent estimates when explanatory variables are correlated
with the disturbance term, which is indeed relevant when estimating policy rules. It is interesting
to note that the correlation of it+π and tx with tε in Eq. (3) is almost always taken into
account in empirical work on time-invariant rules (as typically estimated by the GMM), while it is
almost never considered in the literature on time-varying monetary policy rules (Kim and Nelson,
2006, seem to be the exception). Subsequently, Kim (2006) derives a consistent estimator of the
time-varying parameter model when regressors are endogenous. In line with Kim (2006), we
estimate the following empirical model:
( ) ( ) tttitittt rxrr ερβππαρ ++⎥⎦⎤
⎢⎣⎡ +−+−= −++ 1
*_
1 (4)
ttt rr ϑ+= −1
__, ( )2,0...~ ϑσϑ Ndiit (5)
tjtit Z ϕσξπ ϕ+= −+' , ( )1,0...~ Ndiitϕ (6)
12 We estimated the monetary policy rules including higher lags of interest rates, but failed to find them significant.
Time-Varying Policy Neutral Rate - 16 -
tjtt Zx νσψ ν+= −' , ( )1,0...~ Ndiitν (7)
The measurement equation (4) is a Taylor rule with the policy neutral rate, tr_
, as outlined above.
However, we relax here the assumption of a constant policy neutral rate and let it vary over time,
tr_
, as specified in the transition equation (5), assuming a random walk process without drift.13
Given the data limitations and the fact that our sample is characterized by a relatively stable
institutional structure as well as actual conduct of monetary policy, we do not allow α , β and
ρ to be time-varying. The ‘first-stage’ Eqs. (6) and (7) lay out the relationship between the
endogenous regressors ( it+π and tx ) and their instruments, tZ . The list of instruments, jtZ − , is
as follows: 1−tπ , 12−tπ , 1−tx , 2−tx , 1−tr and +tr (foreign interest rate – 1YEURIBOR). We assume
that the parameters in Eqs. (6) and (7) are time-invariant. Next, the correlation between the
standardized residuals tϕ and tν and tε is εϕκ , and ενκ , , respectively (note that ϕσ and νσ are
standard errors of tϕ and tν , respectively). The consistent estimates of the coefficients in Eq. (4)
are then obtained in two steps. In the first step, we estimate the equations (6) and (7) and save
the standardized residuals tϕ and tν . In the second step, we estimate Eq. (8) along with Eq. (5)
using maximum likelihood via the Kalman filter. Note that (8) now includes bias correction
terms, (standardized) residuals from Eqs. (6) and (7), to address the aforementioned endogeneity
of the regressors. Consequently, the estimated parameters in Eq. (8) are consistent, as tι is
uncorrelated with the regressors.
13 We also experimented with an AR(1) structure in Eq. (5), but it just marginally reduced the likelihood and the estimated AR parameter was very close to one, anyway. In general, it would be possible to link time variation of policy neutral rate to the inflation target, but this would be more demanding on degrees of freedom. I leave this generalization for further research.
Time-Varying Policy Neutral Rate - 17 -
( ) ( ) ttε,t,εtε,tν,εtt*ititt
_
t ισκνσκρrβxππαrρr ++++⎥⎦⎤
⎢⎣⎡ +−+−= −++ ϕϕ11 ,
( )2,
2,
2, )1(,0~ tvt Nι εεϕε σκκ −−
(8)
Several authors (see for example Gerdesmeier and Roffia, 2004) raised the issue of including
additional economic variables such as the (real) exchange rate or money growth in Eq. (3) to try
to capture the state of the economy in a fuller manner. Nevertheless, this is typically done in an
ad hoc manner. On the contrary, when the literature assumes that interest rates depend only on
inflation and output, obviously it does not mean that other variables are neglected. For example,
as Taylor (2001, p. 266) puts it for the exchange rate: “Although the policy rule ... may not appear to
involve interest rate reaction to exchange rate, it implies such a reaction. What might appear to be a closed economy
policy rule is actually just as much an open economy rule as if the exchange rate appeared directly.” In other
words, additional variables that do not enter Taylor rule directly may influence inflation and
output and therefore the interest rate setting indirectly. It is also important to emphasize that CPI
inflation targeting, as opposed to domestic inflation targeting, has an implicit concern for foreign
shocks given the composition of the consumer basket (Svensson, 2000). Therefore, in our paper
we do not introduce foreign disturbances explicitly in the monetary policy rule, but we will
employ them as the instruments in our empirical specification.
Additionally, in a book describing the current forecasting and policy analysis process in the CNB,
Coats et al. (2003) report that no other variables than inflation and the output gap enter into the
monetary policy rule in the CNB Quarterly Projection Model (QPM hereinafter). In particular, as
regards exchange rate fluctuations the CNB has stated several times that it does not directly react
to them. It has acknowledged that the exchange rate plays an important role for inflation
developments in small open economies and that it might react indirectly to exchange rate
Time-Varying Policy Neutral Rate - 18 -
fluctuations if they jeopardize inflation developments (Kotlán and Navrátil, 2003). Regarding
money aggregates, one can expect that an inflation targeting central bank in general views them as
only supplementary information about the degree of economic activity and/or inflationary
pressures, and also does not directly react to them.
Originally, the design of the Taylor rule lacked a forward-looking element characteristic of the
modern monetary policy conduct. An additional way of assessing the sensitivity of our results is
to estimate of both backward- and forward-looking Taylor-type rules. Therefore, we formulate
the monetary policy rule in Eq. (3) in the case of backward-looking policy rule such that we set
0=i , i.e. we use current inflation rate instead of its forecasted value (which is utilized for the
forward-looking policy rule). Another important point has been raised about the timeliness of
information in monetary policy conduct (Orphanides, 2001). Output data are typically revised at a
later stage, but monetary policy is conducted based on the information available at the time.
Therefore, we collect real-time based CNB output gap estimates (note that inflation is not revised
at a later stage by the Czech Statistical Office) and re-estimate the monetary policy rule with the
real-time output gap. Analogously, we use one year ahead CNB’s real-time inflation forecast in
estimating the monetary policy rule.14
There are further modeling issues stemming from the fact that the policy interest rate is not
changed in a continuous fashion. For instance, the CNB Bank Board meets on a monthly basis to
discuss the policy interest rate settings. Besides, the policy rate change itself is not continuous.
Typically, if the rates are changed, the respective magnitude is 0.25 percentage points (or some
multiple of 0.25), even if the change maximizing economic stability according to the model-based
forecast might be of a (slightly) different magnitude. In consequence, the policy rate is not only
14 In general, the specification with inflation forecast and real-time output gap, the rule is using information known to the central bank at the time. However, as we interpolate quarterly output gap figures to the monthly frequency, we introduce information not known at the time of policy decision. As a result, conventional time-varying parameter model is not appropriate even in this case.
Time-Varying Policy Neutral Rate - 19 -
discrete, but also censored. Given the inherent censoring of policy interest rates, the majority of
authors, such as Clarida et al. (1998, 2000) and Adam et al. (2005), rely on using the 3-month
interbank rate as an approximation of the censored policy rate.
On the other hand, Choi (1999) and Carstensen (2006) put forward modeling censoring in the
policy rate directly by employing for example a modified Tobit model and an ordered probit
model, respectively. The advantage of this approach is that it models the interest rate setting
more realistically and does not have to make a simplifying assumption by utilizing the short-term
interbank rate. On the other hand, this stream of literature so far models only censoring in the
policy rate, but it has been stressed that there is also censoring in the policy rate change
(Podpiera, 2006). In addition, censored models are known to be less efficient and the results
based on them do not seem to stand up in sharp contrast to those using short-term interbank
rates (for example, consider the extent of interest rate smoothing). An additional drawback of
this approach in our case is that our main estimation technique is a time-varying parameter model
with endogenous regressors (Kim, 2006) and to our knowledge this technique is simply not
available with a censored dependent variable. Besides, the CNB’s QPM also uses the 3-month
interbank rate instead of the 2-week policy rate as well. Having all the pros and cons of these two
approaches – short-term interbank rate vs. policy interest rate – in mind, we opt to use the short-
term interbank rate in the estimation of the monetary policy rules.
3.2 Data Our sample contains monthly data over the period 2001:1-2006:09 on yearly CPI inflation
( 12−−= ttt ppπ , where tp is the log of the price level at time t), yearly net inflation (price
indexes of regulated goods excluded from the price index; thus nett
nett
nett pp 12−−=π , where net
tp is
the log of the net price level at time t), the output gap ( tx ,the difference between actual and
Time-Varying Policy Neutral Rate - 20 -
potential GDP growth, defined as below), tr , the short-term interbank rate (3M PRIBOR), the
real effective exchange rate, ( )treer , and +tr , the foreign interest rate (1YEURIBOR). We also
use the real-time CNB internal forecasts of CPI ( )ft 12+π and net inflation ( )fnet
t,
12+π , and the output
gap. Three different estimates of the output gap are employed: a) the estimate using HP filter15, b)
the ex-post revised output gap from the CNB’s QPM as of their October 2006 forecast round
and c) the real-time based output gap collected from the CNB’s QPM. The source of our data is
the CNB public database system ARAD (except for inflation forecasts and the two
aforementioned output gap measures – b) and c), which are not available publicly).
All our variables are available on a monthly basis, except the output gap. Following Adam et al.
(2005), we linearly interpolate quarterly estimates of the output gap to monthly values.16 We use
the mid-points of the CNB inflation target. The choice of the 2001-2006 period is motivated to
have as long a sample period as possible, while not rejecting stationarity of all variables at the 5%
significance level (using the KPSS test). More importantly, the real-time output gap and inflation
forecast are not available before 2001. As a further robustness check, we also estimate the
monetary policy rules with net inflation (based on price indexes excluding regulated goods from
the consumer basket) instead of CPI inflation.17
15 The standard smoothing parameter of 1600 has been used. Different smoothing parameters, such as the one suggested by Ravn and Uhlig (2002), had very little impact on the resulting estimates of the policy neutral rate. The output gap derived from HP filter estimates of potential output differs considerably from those used by the CNB – see Chart B.2 in the Appendix. Note that the output gap from the CNB’s QPM is constructed using a multivariate Kalman filter. Generally, output gap estimates based on the HP filter can be viewed as less reliable, but we keep them in our empirical work, as this gap is replicable based on publicly available data, which is not the case with the two other output gap measures derived from the CNB’s QPM. Note also that the HP filter is known to suffer from end-point bias, making it cumbersome for real-time analysis. Another option would be to derive the output gap using some filtering technique in time series domain such as band-pass filter, see Baxter and King (1999). 16 We also used a quadratic match procedure for interpolation. This yields only little differences for the resulting output gap estimates. We are aware that interpolation introduces information not available at the time in the case of the output gap. 17 These results are available upon request. As net inflation is largely correlated with headline CPI inflation (a value of 0.93 in our sample), it is not surprising that the results are quite similar.
Time-Varying Policy Neutral Rate - 21 -
4 Results
4.1 Time-varying Equilibrium Interest Rates Generally, we find that the policy neutral rate decreases over time, as depicted in Chart 1. This is
in line with our conjecture laid out in the introduction. We report all specifications, from the
backward-looking policy rule with the output gap estimated by the HP filter to the forward-
looking rule with the real-time output gap.
Time-Varying Policy Neutral Rate - 22 -
Chart 1 –Policy Neutral Rate Estimates
Time-varying parameter model with endogenous regressors Backward-looking policy rule
1
2
3
4
5
6
2002 2003 2004 2005 2006
1
2
3
4
5
6
2002 2003 2004 2005 2006
1
2
3
4
5
6
2002 2003 2004 2005 2006
Current inflation, output gap – HP filter Current inflation, output gap – ex-post Current inflation, output gap – real-time Forward-looking policy rule
1
2
3
4
5
6
2002 2003 2004 2005 2006
1
2
3
4
5
6
2002 2003 2004 2005 2006
1
2
3
4
5
6
2002 2003 2004 2005 2006
Inflation forecast – real-time, output gap – HP filter
Inflation forecast – real-time, output gap – ex-post
Inflation forecast – real-time, output gap – real-time
‘Conventional’ time-varying parameter model Backward-looking policy rule
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
Current inflation, output gap – HP filter Current inflation, output gap – ex-post Current inflation, output gap – real-time Forward-looking policy rule
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
1
2
3
4
5
6
7
2002 2003 2004 2005 2006
Inflation forecast – real-time, output gap – HP filter
Inflation forecast – real-time, output gap – ex-post
Inflation forecast – real-time, output gap – real-time
Note: The policy neutral rate ±2 standard errors are reported. The measure of the output gap and inflation used for estimation of the policy neutral rate is reported below each chart. The time-varying parameter model is labeled as conventional when it does not account for endogeneity of regressors.
Time-Varying Policy Neutral Rate - 23 -
The results in Chart 1 unambiguously indicate that the policy neutral rate gradually decreased
from around 5% to values around 2% at the end of 2005 and subsequently slightly increased to
around 2.5% over the course of 2006.18 This confirms substantial interest rate convergence to
levels comparable to the euro area countries. For example, Messonier and Renne (2007) estimate
the euro area real equilibrium interest rate around 1% at the end of their sample (i.e. 2002) and
Wintr et al. (2005) find it to have been just below 1% in 2004. If we add to these estimates 2% for
expected inflation – to reflect the European Central Bank definition of price stability, we receive
an estimate of the policy neutral rate of about 3% for the euro area. The results actually suggest
that the Czech policy neutral rate seems to be slightly below the euro area level. This should not
come as a surprise, as actual short-term interest rates were also often below those in the euro area
(except for several months in 2004, Czech rates appear to be below euro area rates from around
mid-2002 onwards).
Detailed parameter estimates of the monetary policy rules are presented in the Appendix – Table
A.2. The degree of interest rate smoothing falls considerably when allowing for time-varying
parameter specification. Compared to the case of the constant policy neutral assumption, which
is estimated by the GMM19, the value of the interest rate smoothing parameter falls from around
0.9 to 0.4. Time-invariant rules thus appear to overestimate substantially the degree of interest
rate smoothing. This complies with the results of Rudebusch (2006), who stresses that once one
accounts for expectations about future monetary policy, the actual policy rate inertia is in fact
quite low. The standard errors of the estimates are in some cases large, probably reflecting the
smaller sample size. The coefficient on inflation is around 0.3 in the case of the backward-looking
18 The increase at the end of sample period is likely to reflect higher inflation expectations of economic agents. The CNB conducts regularly a survey on inflation expectations of financial markets, households and non-financial firms (actual numbers are easily available from CNB website within their public database ARAD). Inflation expectations of financial markets for the 1 year horizon have risen from some 2.5% in mid 2005 to 3% over the course of 2006. Similar pattern is visible also for household’s and firm’s expectations. 19 The GMM results are available upon request.
Time-Varying Policy Neutral Rate - 24 -
specifications (interestingly, it is significant only when we introduce bias correction terms, tϕ and
tν ), and insignificant for the forward-looking specifications. This suggests that central bank
might have found inflation forecasts as uncertain and considered current inflation as more
informative for future inflation developments. This is probably due to structural changes in
transition economy and the associated higher uncertainty. The insignificance of output gap might
stem from similar reasons, too. The coefficient on the output gap is around 0.6 when it is
significant; otherwise it is smaller for other specifications.
Additionally, the results support the usefulness of applying the time-varying parameter model
with endogenous regressors. The bias correction terms, tϕ and tν , in Eq. (8), are typically
significant and the log likelihood improves after their inclusion. Comparing the estimated policy
neutral rate with those implied by the conventional time-varying parameter model, we find that
the resulting difference between these two varies according to the specification of the policy rule
as well as over time. While the median difference is only 0.05 p.p. in absolute terms, the
maximum difference that the inclusion of bias correction terms amounts to is 1.8 p.p.
Chart 3 in the Appendix presents a comparison of the policy neutral rate based on an identical
specification of the policy rule, but estimated either by the time-varying parameter model with
endogenous regressors or by the conventional time-varying parameter model. Denoting the
policy neutral rate estimated by the former method, ertr ,
_, and the latter method, ctr ,
_, Chart A.3
reports the difference between these two. Obviously, if 0,
_
,
_=− ctert rr , the bias correction terms
do not matter at all.20 However, we can see from the results that although the two methods yield
in general rather similar estimates of the policy neutral rate path, there are periods when the bias
20 Obviously, it might be the case that both bias correction terms are statistically significant, but they just “cancel out” their impact on the estimated policy neutral rate. The probability of “canceling out” for each month is virtually zero.
Time-Varying Policy Neutral Rate - 25 -
correction terms matter considerably, i.e. when the policy neutral rate estimates by the
conventional time-varying parameter model do not even lie inside the confidence interval of the
policy neutral rate estimated by the time-varying parameter model with endogenous regressors, as
shown in Chart A.3.
4.2 Monetary Policy Stance and Inflation Developments There is a discussion in the literature about to what extent monetary policy rules provide a useful
framework to evaluate the monetary policy stance and its impact on subsequent inflation. This is
typically done by comparing the actual interest rate setting with the one implied by the rule and
inflation outcomes. For example, Taylor (1999, p. 340) labels the difference as the policy mistakes
(i.e. the residual from the policy rule) and shows that they are well associated with high inflation
or low capacity utilization with the U.S. data. On the other hand, Reynard (2007), analyzing the
U.S. and Swiss data, questions the reliability of the so-called policy mistakes, as he observes rather
weak link between so-called policy mistakes and inflation relative to the inflation target.
Here we analyze a rather different framework to evaluate the usefulness of policy rules. Instead
of focusing on the residual from the policy rule, we analyze the difference between actual interest
rates and the policy neutral rate (“equilibrium rate”) and its impact on subsequent inflation. We
label the difference between the actual interest rate and the policy neutral rate as the monetary
policy stance hereinafter. Amato (2005) shows that there should be negative correlation between
policy stance and future inflation (at the monetary policy horizon), if private sector is tightly
constrained by past outcomes or mostly backward looking. Similarly, Neiss and Nelson (2003)
develop a DSGE model; their results show that real interest rate gap is valuable as an inflation
indicator.
Time-Varying Policy Neutral Rate - 26 -
We leave examination of the link between policy mistakes and inflation outcomes for further
research, as we do not concentrate in this paper on whether the estimated monetary policy rule
provides an accurate description of CNB policy, but rather on the estimation and evolution of
the policy neutral rate. Implicitly, by carrying out this estimation we also address the issue of the
uncertainty surrounding with equilibrium interest rate measurement (see Laubach and Williams,
2003, or Herrmann, Orphanides and Siklos, 2005, on this uncertainty). In this regard, Amato
(2005) emphasizes that the uncertainty in neutral rate estimates makes it difficult to use them as a
reliable indicator of excess demand pressures, but the theories of neutral interest rates still remain
a useful concept for the formulation of monetary policy.
So, our simple test here is to examine whether our estimated policy neutral rate is helpful in
predicting future inflation developments. If the policy neutral rate is too uncertain a measure,
then it is more likely that it does not provide information for subsequent inflation. Our
supposition is thus that when the actual interest rate is above the policy neutral rate, the future
inflation rate is then likely to fall, as monetary policy can be considered restrictive, and vice versa.
We label the difference between the actual interest rate and the policy neutral rate as the
monetary policy stance hereinafter.
The generic monetary policy stance together with current inflation is plotted in Chart 2.21 As we
also have confidence intervals for the policy neutral rate, it is possible to evaluate whether the
stance was statistically different from zero. The results indicate that monetary policy during the
sample period can be regarded as relatively easy, especially around the years 2002-2003. This
should not come as a surprise, since inflation was well below the target and even got into
21 The monetary policy stance presented in Chart 2 is based on a monetary policy rule specification with an inflation forecast and an ex-post GDP gap. Different specifications play a rather minor role in the overall assessment of the monetary policy stance. In addition, it is interesting to note that the significance of monetary policy stance is not affected by whether we employed the backward or the forward looking policy rule. In addition, Charts A.4 plots monetary policy stance and inflation and Chart A.5 provides a comparison of actual and neutral rate.
Time-Varying Policy Neutral Rate - 27 -
negative numbers for several months in 2003 and the output gap was negative, reaching its
minimum in mid-2003 according to the CNB output gap estimates (see Chart A.1).
Chart 2 – Monetary Policy Stance and Inflation
-1.2
-0.8
-0.4
0.0
0.4
0.8
-2
0
2
4
6
2001 2002 2003 2004 2005 2006
Policy stance Inflation
Note: Positive values refer to monetary policy tightening, while negative values point to policy easing. Policy stance values are on the left axis, while inflation is on the right axis.
As there are transmission lags between a monetary policy action and its impact on the bank’s
targeted variables, we assume that the current monetary policy stance affects inflation after 12 to
24 months. This coincides well with the CNB monetary policy horizon, as the bank
acknowledges that “…interest rate changes have their greatest impact on inflation some 12 to 18 months …”
(CNB). It is also supported by the empirical findings of Borys-Morgese and Horvath (2007).
Within their factor augmented VAR framework, they find that the peak response of inflation to
interest rate shocks is around a year or so (note the maximum reaction of non-tradable inflation
is found to be close to two years). Based on this evidence, it seems to be fruitful to analyze the
horizon between about one and two years. Here we broadly follow the empirical specification by
Moser et al. (2007), which is a variant of Stock and Watson (1999). While these two studies
examine the role of factor models for inflation forecasting, we analyze the impact of the
Time-Varying Policy Neutral Rate - 28 -
monetary policy stance instead. More specifically, we test the significance of monetary policy
stance indicator, including it in an autoregressive-type model for the inflation process.
Our estimation framework begins with the following regression:
itt
_
tit rrπ ++ +⎟⎠⎞
⎜⎝⎛ −+= υφφ 10 (9)
where itπ + is yearly inflation i months ahead, where i=12,….,24. Next, we control for the lagged
inflation terms:
it
n
hhtht
_
tit πrrπ +=
−++ ∑ ++⎟⎠⎞
⎜⎝⎛ −+= υφφφ
1110 (10)
where for simplicity we set n=4.22
Using Eqs. (9) and (10), we investigate the information content of the monetary policy stance on
the future level of inflation. We also re-specify the above equations to address the future change
in the inflation rate as follows:
itt
_
ttit rrππ ++ +⎟⎠⎞
⎜⎝⎛ −+=− υφφ 10 (11)
it
n
hhtht
_
ttit πrrππ +=
−++ ∑ ++⎟⎠⎞
⎜⎝⎛ −+=− υφφφ
1110 (12)
22 We also included higher lags, but with little impact on the results.
Time-Varying Policy Neutral Rate - 29 -
Table 1 – Monetary Stance and Future Level of Inflation
itt
_
tit rrπ ++ +⎟⎠⎞
⎜⎝⎛ −+= υφφ 10
Note: Robust standard errors. ***, ** and * indicate significance at 1, 5 and 10%, respectively.
Table 2 – Monetary Stance and Future Level of Inflation,
Controlling for Lagged Inflation
it
n
hhtht
_
tit πrrπ +=
−++ ∑ ++⎟⎠⎞
⎜⎝⎛ −+= υφφφ
1110
i 0φ 1φ 2φ 3φ 4φ 5φ Adj. R2
12 3.11*** 0.72 0.08 0.07 0.02 -0.67** 0.30 13 3.06*** 0.52 -0.06 0.23 0.13 -0.54* 0.35 14 2.88*** 0.18 -0.11 0.07 0.08 -0.53* 0.40 15 2.64*** -0.19 -0.27* 0.24 -0.06 -0.33 0.45 16 2.42*** -0.45** -0.13 -0.12 -0.20 0.02 0.47 17 2.37*** -0.28* -0.27 -0.19 0.18 -0.06 0.49 18 2.31*** -0.62** -0.59** 0.19 0.16 -0.08 0.49 19 2.18*** -0.76** -0.62** 0.16 0.33 -0.15 0.49 20 2.15*** -0.77** -0.66** 0.31 0.26 -0.16 0.45 21 2.00*** -0.91*** -0.55 0.27 0.21 -0.07 0.45 22 1.84*** -1.07*** -0.50* 0.22 0.01 0.15 0.47 23 1.77*** -1.07*** -0.35 -0.06 0.04 0.31 0.45 24 1.77*** -1.03*** -0.44 0.02 0.01 0.36 0.42
Note: Robust standard errors. ***, ** and *indicate significance at 1, 5 and 10%, respectively.
i 0φ 1φ Adj. R2
12 1.29*** -1.23** 0.16 13 1.19*** -1.47** 0.24 14 1.16*** -1.60*** 0.28 15 1.16*** -1.68*** 0.31 16 1.16*** -1.76*** 0.35 17 1.16*** -1.82*** 0.36 18 1.21*** -1.77*** 0.36 19 1.26*** -1.69*** 0.34 20 1.33*** -1.57** 0.30 21 1.44*** -1.39*** 0.25 22 1.53*** -1.25** 0.22 23 1.62*** -1.10* 0.19 24 1.71*** -0.96 0.16
Time-Varying Policy Neutral Rate - 30 -
Table 3 – Monetary Stance and Future Change of Inflation
itt
_
ttit rrππ ++ +⎟⎠⎞
⎜⎝⎛ −+=− υφφ 10
i 0φ 1φ Adj. R2
12 -2.18*** -5.23*** 0.69 13 -2.26*** -5.48*** 0.71 14 -2.28*** -5.62*** 0.72 15 -2.28*** -5.71*** 0.74 16 -2.28*** -5.79*** 0.75 17 -2.27*** -5.84*** 0.76 18 -2.22*** -5.81*** 0.74 19 -2.19*** -5.73*** 0.73 20 -2.12*** -5.62*** 0.72 21 -2.03*** -5.43*** 0.69 22 -1.96*** -5.30*** 0.72 23 -1.91*** -5.19*** 0.72 24 -1.84*** -5.07*** 0.72
Note: Robust standard errors. ***, ** and *indicate significance at 1, 5 and 10%, respectively.
Table 4 – Monetary Stance and Future Change of Inflation,
Controlling for Lagged Inflation
it
n
hhtht
_
ttit πrrππ +=
−++ ∑ ++⎟⎠⎞
⎜⎝⎛ −+=− υφφφ
1110
i 0φ 1φ 2φ 3φ 4φ 5φ Adj. R2
12 2.93*** 0.60 -1.08*** -0.03 0.46 -0.77** 0.81 13 2.85*** 0.41 -1.24*** 0.12 0.34 -0.64* 0.83 14 2.68*** 0.07 -1.28*** -0.05 0.55 -0.62* 0.84 15 2.48*** -0.28 -1.45*** 0.11 0.44 -0.47 0.86 16 2.26*** -0.54** -1.30*** -0.20 0.33 -0.12 0.86 17 2.20*** -0.63** -1.46*** -0.32 0.71 -0.21 0.88 18 2.16*** -0.71** -1.78*** 0.08 0.67 -0.22 0.87 19 2.01*** -0.86*** -1.82*** 0.06 0.83 -0.28 0.86 20 1.97*** -0.87*** -1.87*** 0.21 0.76 -0.29 0.85 21 1.84*** -1.00*** -1.75*** 0.08 0.74 -0.21 0.85 22 1.67*** -1.17*** -1.72*** 0.12 0.52 0.02 0.85 23 1.62*** -1.16*** -1.56*** -0.10 0.43 0.21 0.86 24 1.61*** -1.12*** -1.65*** -0.03 0.42 0.28 0.86
Note: Robust standard errors. ***, ** and *indicate significance at 1, 5 and 10%, respectively.
The results from Eq. (9) are given in Table 1. Our definition of the monetary policy stance seems
to be informative for future inflation, explaining typically about 1/4-1/3 of its variance. These
results are largely confirmed when controlling for the lagged inflation terms, as suggested by the
Time-Varying Policy Neutral Rate - 31 -
estimation of Eq. (10) presented in Table 2. Similarly, the policy neutral rate seems to be a
relatively good predictor of the future change of the inflation rate, as presented in Table 3. This
result is largely robust to the inclusion of lagged inflation as well (see Table 4).23 All in all, the
results support the usefulness of the policy neutral rate in understanding the future behavior of
inflation.24
5 Conclusions This paper analyzes the policy neutral rate in the Czech Republic. In order to do so, we estimate
various specifications of simple Taylor-type monetary policy rules at monthly frequency from
2001:1 to 2006:9. To address the sensitivity of results, the specifications differ based on whether
we include real-time or ex-post revised data, employ backward or forward-looking monetary
policy rules or vary the measure of the output gap.
As we focus on the fluctuations of the policy neutral rate over time, we use a time-varying
parameter model with endogenous regressors (Kim, 2006). This approach is especially appealing
for estimating monetary policy rules, as it addresses the endogeneity of inflation (forecast) and
output gap. Indeed, the results support the usefulness of applying the time-varying parameter
model with endogenous regressors. The bias correction terms, accounting for the endogeneity of
the regressors, are typically significant. The log likelihood improves after their inclusion and the
23 Several papers on the predictability of asset returns emphasize issues related to small sample inference (Kirby (1997), Kilian (1999) or Ang and Bekaert (2007)). In these applications, results based on conventional estimators of standard errors that appear significant often prove to be spurious. Typically, the bias grows with the forecasting horizon. On the other hand, our results do not suggest that the significance of policy stance and inflation increases with the forecasting horizon, but the significance rather reflects the monetary policy horizon of the CNB. We thank anonymous referee for this point. 24 We also tested the robustness of our results by including other macroeconomic variables in Eqs. (10) and (12) such as the real effective exchange rate, credit and monetary aggregates. The results remain largely unchanged and are available upon request. We also assess, if the predictive content of neutral rate comes from the “trend component” of monetary policy rule. In order to do so, we estimate a policy rule without inflation and output included. The resulting monetary policy stance fluctuates around zero with no clear pattern and also has no predictive content for inflation.
Time-Varying Policy Neutral Rate - 32 -
estimated path of the policy neutral rate is for certain periods considerably different from the
estimates ignoring the endogeneity.
The results indicate that the policy neutral rate decreases gradually over the course of the sample
period from around 5% in 2001 to about 2.5% in 2006, showing substantial interest rate
convergence to levels comparable to the euro area. Over the longer term, the decrease may be
supported by a number of factors such as capital accumulation, decrease in the risk premium, real
equilibrium exchange rate appreciation as well as successful disinflation of the Czech economy
and well-anchored inflation expectations.
One of our primary policy applications, besides measuring the policy neutral rate by a novel
technique, is also to propose a measure of the monetary policy stance based on the difference
between the actual interest rate and the policy neutral rate. Our results indicate that this measure
is quite useful in predicting future inflation developments, i.e. the monetary policy stance
indicator affects the level as well as the change of the future inflation rate. In terms of future
research, it would be interesting to see more evidence on other inflation targeting countries to
uncover whether our proposed monetary policy stance measure remains a useful predictor of
future inflation developments, as we find in the case of the Czech Republic.
Time-Varying Policy Neutral Rate - 33 -
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Time-Varying Policy Neutral Rate - 37 -
http://www.cnb.cz/www.cnb.cz/en/about_cnb/missions_and_functions/mp_faktsheet_en.htm
l
Time-Varying Policy Neutral Rate - 38 -
APPENDIX
Chart A.1 – Interest Rate, Output Gap, Inflation and Inflation Target
-2
-1
0
1
2
3
4
5
6
2001 2002 2003 2004 2005 2006
Interest rateInflation
Inflation targetOutput gap
Note: This chart presents current inflation, the short-term interbank interest rate (3M PRIBOR) and the CNB output gap as of the October 2006 forecast round.
Chart A.2 – Comparison of Output Gap Estimates
-3
-2
-1
0
1
2
2001 2002 2003 2004 2005 2006
Gap - ex post Gap - real-time Gap - HP filter
Note: This chart presents the three measures of the output gap used in the paper: the output gap estimated by the CNB as of their October 2006 forecast round (Gap - ex post), the real-time based output gap estimated by the CNB (Gap - real-time) and the output gap calculated using the HP filter (Gap – HP filter) as the estimate of potential output.
Time-Varying Policy Neutral Rate - 39 -
Chart A.3 – Importance of Bias Correction Terms in Estimating Policy Rules
Backward-looking policy rule
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
2001 2002 2003 2004 2005 2006-.6
-.4
-.2
.0
.2
.4
.6
.8
2001 2002 2003 2004 2005 2006-2
-1
0
1
2
3
2001 2002 2003 2004 2005 2006
Current inflation, output gap – HP filter
Current inflation, output gap – ex-post
Current inflation, output gap – real-time
Forward-looking policy rule
-.8
-.6
-.4
-.2
.0
.2
.4
2001 2002 2003 2004 2005 2006-.8
-.6
-.4
-.2
.0
.2
.4
2001 2002 2003 2004 2005 2006-.8
-.6
-.4
-.2
.0
.2
.4
.6
.8
2001 2002 2003 2004 2005 2006
Inflation forecast – real-time, output gap – HP filter
Inflation forecast – real-time, output gap – ex-post
Inflation forecast – real-time, output gap – real-time
Note: The difference between the policy neutral rates estimated from the time-varying parameter
model with endogenous regressors, ertr ,
_, with its confidence intervals, and from the conventional
time-varying parameter model, ctr ,
_
. The measure of the output gap and inflation used for the
estimation of the policy neutral rate is reported below each chart. Consequently, if the confidence
intervals are different from zero, it means that ctr ,
_
does not lie within the confidence intervals of
ertr ,
_.
Time-Varying Policy Neutral Rate - 40 -
Chart A.4 – Actual Interest Rate and Policy Neutral Rate
1
2
3
4
5
6
2001 2002 2003 2004 2005 2006
Neutral rate Actual rate
Table A.1 – KPSS Test
Series Test statistic
PRIBOR 3M 0.355*
CPI Inflation 0.165
CPI Inflation forecast (t+12) 0.163
Net Inflation 0.147
Output gap – HP filtered 0.106
Output gap – Real-time 0.293
Output gap – Ex-post 0.214
M2 growth 0.168
Real effective exchange rate 0.447*
The null hypothesis is that the series is level stationary. Critical values for the null hypothesis: 10% - 0.347, 5% - 0.463, 1% - 0.739. Sample period: 2001:1-2006:09. *, **, *** denote significance at the 10, 5 and 1 percent level, respectively.
Time-Varying Policy Neutral Rate - 41 -
Table A.2 – Monetary Policy Rules Estimation
( ) ( ) tttitittt rxrr ερβππαρ ++⎥⎦⎤
⎢⎣⎡ +−+−= −++ 1
*_
1
ttt rr ϑ+= −1
__, ( )2,0...~ ϑσϑ Ndiit
ttε,t,εtε,tν,εt ισκνσκ ++= ϕε ϕ , ( )2,
2,
2, )1(,0~ tvt Nι εεϕε σκκ −−
Model Parameters 1 2 3 4 5 6 7 8 9 10 11 12
ρ 0.40*** 0.40*** 0.40*** 0.41*** 0.40*** 0.40*** 0.42*** 0.40*** 0.42*** 0.42*** 0.45*** 0.40***
(0.02) (0.09) (0.06) (0.06) (0.07) (0.09) (0.11) (0.10) (0.10) (0.09) (0.10) (0.15)
α 0.27*** 0.07 0.28*** 0.06 0.28*** 0.07 -0.15 -0.15 -0.18 -0.16 -0.17 -0.14
(0.07) (0.07) (0.08) (0.06) (0.07) (0.06) (0.12) (0.11) (0.11) (0.11) (0.10) (0.09)
β 0.21 0.11 -0.06 0.57** -0.06 -0.06 0.12 0.14 0.66** 0.66** 0.18 -0.02
(0.24) (0.24) (0.28) (0.29) (0.22) (0.23) (0.27) (0.25) (0.28) (0.31) (0.23) (0.19)
ν,εκ -0.06*** -0.07*** -0.06*** 0.01 0.02 0.02
(0.01) (0.02) (0.02) (0.01) (0.01) (0.01)
,εκϕ -0.02* -0.02* -0.02* 0.01 -0.01 -0.02***
(0.01) (0.01) (0.01) (0.01) (0.02) (0.01)
AIC -1.10 -1.00 -1.07 -1.00 -0.95 -0.94 -0.91 -0.94 -0.94 -0.96 -0.91 -0.88 Note: Robust standard errors in brackets. ***, ** and *indicate significance at 1, 5 and 10%, respectively. The models differ according to whether bias correction terms are included and the specification of it+π and tx . it+π is either the CNB inflation forecast one year ahead (abbreviated as IF below) or the current inflation rate (IC). tx is a measure of the output gap: 1. as estimated by HP filtering (HP), 2. CNB ex-post output gap measure based on multivariate Kalman filter procedure (EX), 3. CNB real-time output gap measure based on multivariate Kalman filter procedure (REAL). Models 1 and 2 = IC, HP; Models 3 and 4 = IC, EX; Models 5 and 6 = IC, REAL; Models 7 and 8 = IF, HP; Models 9 and 10 = IF, EX and Models 11 and 12 = IF, REAL;
Time-Varying Policy Neutral Rate - 42 -
Price Setting and Market Structure - 43 -
2 Price Setting and Market Structure: An Empirical Analysis of Micro Data *
2.1 Introduction
There is a large and growing body of empirical literature based on micro data on price setting in
Eurozone countries, with a view to uncover microeconomic sources of price inertia and possible
asymmetries across countries (e.g. the “Inflation persistence network”, see Dhyne et al., 2006). By
contrast, there is very little evidence based on comprehensive micro data on price setting in New
European Union (EU) members (or other emerging economies). In this paper, we analyze actual
prices for a wide range of products, which form a large proportion of the consumer basket in
Slovakia. In addition to the richness of our dataset, the analysis of a country like Slovakia has two
interesting implications.
First, in the sample period Slovakia experience an average rate of inflation near two-digit levels,
in contrast to previous studies focusing on low inflation countries. In principle, pricing policies
can be quite different from those in more stable macroeconomic environment (Calvo et al., 2002).
Second, there is a debate in Europe on the potentially large asymmetries between price rigidity in
the new as opposed to old EU members. These asymmetries would imply sharply asymmetric
effects of monetary policies for New member states when they will eventually join the Eurozone
(Elbourne and de Haan, 2006). The presence of strong asymmetries would call for delaying entry
in the Eurozone. One of the main objectives of our analysis is indeed to verify the view
according to which a country like Slovakia, undergoing a process of massive structural change
and market liberalization, is characterized by a more rigid price system, inducing a higher degree * We thank Ian Babetskii, Mihai Copaciu, Mario Holzner, Július Horváth, Attila Rátfai, Stanislav Vidovič†, Sangeeta Pratab, Zoltán Wolf, Petr Zemčík and the seminar participants at the European Economic Association Annual Congress, Hungarian National Bank 5th Macro Workshop, Czech Economic Association Annual Conference, the Deutsche Bundesbank, Inflation Persistence Network Follow-up Meeting, and Charles University (Prague) for valuable comments. We are especially grateful to Július Horváth for providing us with the data. This research was supported by a grant from the CERGE-EI Foundation under a programme of the Global Development Network. All opinions are those of authors and have not been endorsed by CERGE-EI or the GDN.
Price Setting and Market Structure - 44 -
of inertia (persistence) in price dynamics (Dhyne et al., 2006). In fact, this view does not find
empirical support in the case of Slovakia. By comparing price inertia for different sectors,
characterized by different market structures, namely manufacturing vs. services, we find that a
lower degree of market competition is associated with higher price dispersion and lower
persistence. This should not come as a surprise, as in the well-known staggered price model of
Calvo (1983) as market competition increases inertia tends to increase rather than decrease (see
Calvo, 2000). This result has relevant implications as the process of integration of Slovakia in the
EU, and the attendant higher degree of competition in goods markets, is likely to increase rather
than decrease inflation inertia.
The paper is organized as follows. In section 2, we discuss a simple analytical framework, which
will serve as basis for the empirical analysis. In section 3, we study price setting behaviour of 423
narrowly defined products. More specifically, we estimate the frequency and magnitude of price
changes, price dispersion and inflation persistence at the level of price setter using a unique
dataset, covering a large component of the Slovak consumer price index (CPI) during the period
1997-2001. We identify the factors that affect price setting behaviour, namely, the determinants
of frequency and size of price changes, the level of inflation persistence and price dispersion.
Section 4 concludes and provides some policy implications. Appendix contains the formal
definitions of price setting descriptive statistics.
2.2 Price Setting Behavior Although there is disagreement on the relevant theoretical model for price setting, Calvo’s
staggered price model has become a benchmark in the literature. In its reduced form, Calvo’s
model gives a useful framework for empirical analysis.
Calvo price setting has several virtues: first, it can accommodate the case of perfect competition
and price flexibility as a limiting case; second, it highlights factors affecting inertia that are likely
to be relevant in most theories of price adjustment; third, it allows to distinguish factors related
Price Setting and Market Structure - 45 -
to market structure from those linked to aggregate macroeconomic variables, that in turn are
affected by policy.
As an illustration of the framework, consider the well-known New Keynesian Phillips curve
(NKPC) obtained from a discrete time version of Calvo’s model. As in Calvo (1983), firms
change their prices at random intervals, so that at each point in time there is a fraction α of firms
that set new prices and a fraction 1-α that keep their prices unchanged. New prices are set
optimally by firms facing a downward-sloping demand function with price elasticity -θ. Denoting
with π the rate of inflation, such a NKPC relates current inflation to expected inflation and to
current output gap (Yt-Ytⁿ):
( )( )[ ] ( )ntttt YE −−−+= + t1 Y11 ςαβααπβπ
where β is the discount factor and ς a coefficient that characterizes the link between marginal
costs and output. It is easy to show that the parameter ς is a decreasing function of the parameter
θ, that measures the degree of competition in goods markets (see Woodford (2003) and Calvo
(2000)). As markets become more competitive, ς declines. Under perfect competition (θ→∞) ς
becomes zero.
The model can be closed by deriving the dynamics of the output gap and assuming a policy rule
determining the interest rate.
Focusing on two parameters of the model: α, the frequency of price adjustment and, θ, the
elasticity of substitution among goods, Calvo’s model has two main implications: first the higher
is α , the faster is the adjustment (lower inertia); second, and much less noted in the literature
(Calvo 2000 is the exception), the higher is θ, thus the more competitive are goods markets, the
slower is the adjustment, and thus the higher is inertia. Note that in the simplest version of the
model it is assumed that α is a constant. In Yun (1996) α is instead a function of the average
inflation rate. In addition, α could be affected by market structure, and increase with market
competition. Although more controversial, we assume in the empirical analysis that this channel
could be at work, and thus, through higher α market competition reduces inertia. At the same
Price Setting and Market Structure - 46 -
time, market competition increases inertia through the parameter θ. The mechanism is related to
the so-called strategic complementarity in price behavior and thus has to do with interaction
among price setters (Woodford 2003). The intuition is that as competition increases firms will
tend to “follow the pack”, as deviations from the average price may push the firm out of the
market (see Calvo, 2000).25
Summing up, increasing market competition may increase or decrease inertia (persistence)
depending on the relative strength of two conflicting effects. One plausible assumption would be
that the effect of varying α with average inflation is rather weak: it is necessary to have a
substantial increase in average inflation to significantly modify price behavior in terms of
frequency of adjustment (Golosov and Lucas, 2007).
2.3 Empirical analysis 2.3.1 Dataset The dataset contains the price records of 604 products collected at monthly frequency by the
Slovak Statistical Office (SSO) in 38 districts in 1997-2001. For each record in the dataset, there
is information on the date (month and year), district, product category code and the price of item.
The data allow tracking individual price dynamics. The price for each product is collected
monthly at several stores in the district, but typically only from the capital town of the district.26
On average, about five stores are monitored in a particular district. As a result, each product
contains around 10,000 records. Considering all products, the dataset contains more than 5
millions observations. The dataset contains actual prices as opposed to quoted prices or price
indices. The final dataset has been reduced, excluding those products that featured a relatively
25 Note also that the deviation from the price of competitors has been found as one of the most important obstacles for price adjustment in surveys of euro area firms (see Fabiani et al., 2006). 26 See Horvath and Vidovic (2004) for more details on the methodology of data collection at the SSO.
Price Setting and Market Structure - 47 -
high share of missing records and regulated prices that account for up to 20 percent of the total
CPI basket.27
Table 1 indicates 12 categories in which products are classified, and reports the original weight of
a given product category in the CPI basket, sample weight and the number of products in the
sample for each category in the dataset.
Table 1 – Coverage of the Dataset
Original Weights
Sample Weights
Number of products in the sample
Food and Non-Alcoholic Beverages 23.60 36.17 121 Alcoholic Beverages and Tobacco 6.98 12.05 10 Clothing and Footwear 7.51 8.43 68 Housing, Water, Gas and Electricity 21.50 4.85 14 Furnishing & Maintenance of Housing 5.18 3.6 70 Health Care Expenses 1.45 0 0 Transport 9.25 9.75 20 Communications 2.73 0 0 Leisure and Culture 7.21 9.19 56 Education 0.58 0 0 Hotels, Cafés and Restaurants 7.22 9.42 30 Miscellaneous Goods and Services 6.79 6.54 34 Total 100 100 423
2.3.2 Evidence This section contains evidence on price setting behavior in Slovakia. First, we characterize the
dynamics of Slovak inflation and analyze its persistence at various levels of aggregation. Second,
we estimate additional pricing statistics28 such as the frequency and size of price changes and
examine correlations among them. Third, we study the factors determining price setting behavior.
2.3.2.1 Inflation Dynamics and Persistence
27 In general, see Aucremanne and Dhyne (2004) for a comprehensive discussion of methodological issues related to the censored nature of dataset. 28 The detailed product-specific results are available upon request from the authors.
Price Setting and Market Structure - 48 -
The average annual CPI inflation rate in Slovakia has been about 9% in the period 1998-2001.
There is a notable hike in the inflation rate starting in mid 1999, which has been caused mainly by
price deregulations and regulated prices increases. This one-off shock has merely changed the
price level and thus largely vanished after one year. From the end of 2000, there is a gradual
slowdown in the inflation rate to some 6% at the end of the sample period.
Figure 1 – Official CPI inflation and sample inflation, 1998-2001
2
4
6
8
10
12
14
16
18
98:3 99:1 99:3 00:1 00:3 01:1 01:3
FULL SAMPLE
Source: Slovak Statistical Office, own calculations
Figure 1 reports the Slovak official CPI inflation and our sample inflation. The sample contains
57% of the full CPI basket. The sample inflation is lower than aggregate inflation by 3.3
percentage points on average. The difference between official and sample inflation is attributable
chiefly to the rate of increase in regulated prices.29
Next, we examine the degree of inflation inertia by applying a non-parametric measure by
Marquez (2004) and Dias and Marquez (2005). This approach builds on the idea that a less
persistent inflation is more likely to cross its long-run (possibly time-varying) mean of inflation
29 For example, from January 1999 to January 2000 regulated prices increased by 33%. Given the weight in the CPI index of 17.8% (see Monetary Survey of National Bank of Slovakia, January 2000), this contributed 5.9 percentage points to the official inflation and as such, it almost fully explains the difference between official and our sample inflation.
Price Setting and Market Structure - 49 -
rate. Specifically, a measure of persistence γ is calculated as follows: Tn /1−=γ , where n is
the number of times the series crosses its long-run mean and T is the number of observations.
Given the length of our sample, we opt for a simple univariate filter (Hodrick-Prescott filter) to
approximate the long-run time-varying mean.30
This non-parametric approach has several attractive features over more common parametric
measures, typically based on the sum of autoregressive parameters in the regression of inflation
on its lagged values. Dias and Marquez (2005) derive finite sample and asymptotic properties of
this non-parametric measure. When conducting Monte Carlo simulations, they find that the bias
of the estimate of persistence based on non-parametric approach is smaller for any sample size,
as compared to the parametric measure. Besides, they argue that non-parametric measure is more
robust to structural breaks and additive outliers, which is appealing in case of analysis of
emerging market economies. Marquez (2004) shows that the values of γ close to 0.5 indicate
absence of persistence in the series. Values significantly above 0.5 signal a positive
autocorrelation in the series, while values substantially below 0.5 signal negative autocorrelation.
Figure 2 - Inflation Persistence, 423 products
0
10
20
30
40
50
60
70
80
0.5 0.6 0.7 0.8 0.9 1.0
30 The standard smoothing parameter of 14400 has been used for all products. Different smoothing parameters, such as the one suggested by Ravn and Uhlig (2002), had very little impact on the resulting estimates.
Price Setting and Market Structure - 50 -
The results presented in Figure 2 indicate that out of 423 products, only 4 of them display no
persistence (γ <0.66 in our case, as ( )5.02 −γT approximately distributed as N(0,1)), while the
remaining 419 products display positively persistent inflation, at the 5% significance level.
The results on the degree of inflation persistence are reported in Table 2. Given asymmetries
within the sectors, we average the product-level inflation persistence, instead of estimating
persistence on sectoral data directly, to reduce the potential aggregation bias (see Granger, 1980
and Zaffaroni, 2004). Indeed, the estimate of persistence of aggregate inflation is somewhat
greater than the average of estimates at the product-specific level (0.9 vs. 0.87). All sectors display
very high persistence, with the persistence in services inflation being somewhat lower.31 This is
surprising, as the services production is relatively more labor intensive and as such, it is expected
that the persistence should be greater than for the other sectors. It is noteworthy that also Clark
(2006) does not find persistence in the services sectors in the U.S. to be greater than other
components of consumer prices and, for some specifications, services show the smallest
persistence. Similarly, the results of Lunnemann and Matha (2004) for 15 EU countries indicate
that services dummy is significantly negative in nearly all their fixed effects regressions of the
determinants of persistence32 (see also Altissimo et al., 2007 for evidence that services often
exhibit smaller persistence than other products in the consumer basket). However, these papers
do not provide an explanation for this ”services inflation persistence puzzle”.
Even though we do not want to overestimate the robustness of this result, it is remarkable that
services, a sector typically characterized by a lower degree of competition, does not display higher
persistence in inflation. It is worth noting that Calvo’s model of staggered prices predicts such
result, as higher degree of competition in the market produces a process of “follow the pack”,
31 The difference in the results between the expenditure-weighted and non-weighted persistence in services inflation is largely driven by a single item ‘complete lunch in a factory canteen’. This item’s inflation persistence stands at 0.71 and its sample weight is 6.9%. 32 The same authors in a different study, Lunnemann and Matha (2005a) however report that if they exclude services from consumer prices then the estimated inflation persistence of the remaining basket declines in comparison to the full basket.
Price Setting and Market Structure - 51 -
which is consistent with a high degree of price homogeneity across firms (Calvo, 2000). When
markets are highly competitive individual prices cannot diverge much from the average, as firms
would loose large shares of their market. In the limiting case of perfect competition, prices would
be exactly the same across firms. This effect arises because the degree of strategic
complementarity increases with higher competition, implying that the strategy of an individual
price setting firm is an increasing function of the average strategy (price) in the market (see also
Woodford, 2003). Therefore, although competition reduces costly dispersion in prices, from a
dynamic perspective it may increase persistence.33
Table 2– Inflation Persistence
No. of Products
Sample Weights
Inflation Persistence
Inflation Persistence – Weighed
Processed Goods 375 79.28 0.874 0.867 Raw Goods 48 20.72 0.846 0.875 Perishables 64 23.65 0.862 0.869 Durables 231 36.39 0.874 0.886 Non-durables 136 49.62 0.874 0.876 Services 56 13.99 0.851 0.796 Total 423 100 0.871 0.868
Notes: Raw goods category contains meats, fruits, vegetables, milk, cream, honey, eggs, salt, mineral water, gasoline, fuel oil, motor oil and coolants. The results in the last column are expenditure-weighted. Non-durables contain mainly food and beverages. Services include mainly the category ‘Hotels, cafés and restaurants’ and fees and repairs for various categories of products. Durables contain the remaining products. Perishables are a sub-category of food. 2.3.2.2 Frequency of Price Changes Table 3 shows that the estimated expenditure-weighted frequency of price changes is 0.34 in our
sample. This means that approximately one in every three consumer prices is changed in a given
month. It implies that the expenditure-weighted average duration of a price spell is 3.75 months
(and 4.2 months without CPI weights). As the distribution of the duration is asymmetric, the
median duration reaches 3.9 months.34 Thus, consumer prices in Slovakia change more often
than the one year frequency often found for the advanced market economies (see Dhyne et al.,
33 Coricelli (2005) discusses the implications of this issue for the conduct of monetary policy. An overview on inflation persistence and the conduct of monetary policy is presented in Levin and Moessner (2005). 34 Additional results using median, weighted median and simple average to estimate the frequency are available on a request.
Price Setting and Market Structure - 52 -
2006). The greater frequency of price changes in Slovakia, as compared to advanced market
economies, is likely due to a higher inflation rate, as well as a smaller share of services in the
consumer basket. Next, frequent price adjustments may also reflect structural changes in the
economy. In such a context, demand tends to be more uncertain and, consequently, firms have to
experiment to find their optimal price to be set.35 The probability that the single price spell would
last longer then 12 months is essentially zero. More specifically, there are only 3 out of 423
products having the average duration of price spells longer than one year.
There is a considerable degree of heterogeneity in terms of the frequency of price changes.
Products such as fruits and vegetables or gasoline typically change their price less than bimonthly.
On the other hand, several services keep the price fixed for almost 2 years. The duration of a
price spell is more than 7 months for services. This is likely due to the fact that labor-intensive
services are typically less exposed to international competition. Furthermore, as noted by Bils and
Klenow (2004), the lower variability of demand for services can be behind their prolonged
inaction in price adjustment. At the other extreme, prices change most frequently for the raw
goods. Diversification of inputs for raw goods is typically limited, as compared to processed
goods and thus price changes are triggered more often.
Table 3 - Frequency of Price Changes
No. of products
Sample Weights
Average Frequency
Average Duration
Processed Goods 375 79.28 0.28 4.3 Raw Goods 48 20.72 0.6 1.83 Perishables 64 23.65 0.46 2.43 Durables 231 36.39 0.34 3.76 Non-durables 136 49.62 0.35 3.75 Services 56 13.99 0.15 7.25 Total 423 100 0.34 3.75
Notes: Frequency refers to the frequency of price changes, i.e. empirical probability that price of the product will change. Duration indicates the number of months between price changes. CPI weights are used for weighting. See Table 2 for the classification of products into categories.
35 Rothschild (1974) presents a model in which monopolistic price setter learns gradually about optimal price in a noisily observed demand.
Price Setting and Market Structure - 53 -
The frequency of price changes between 0.1-0.4 is far more common than other frequencies (see
Figure 3). There are only four products, truly flexible prices, changing the price more often than
in 80% of the cases. Overall, this evidence shows that one cannot simply refer to a given measure
price stickiness or price flexibility, as the degree of price stickiness varies dramatically across
products. The distribution of the frequency price changes is skewed to the right, similarly to what
has been found for other countries (see e.g. Diaz et al., 2004, for comparable evidence on
Portugal and Baharad and Eden, 2004, for Israel).
Figure 3 - Frequency of Price Changes, Histogram
0
10
20
30
40
50
60
0.25 0.50 0.75
Table 4 puts our results in an international perspective. While the distribution of price stickiness
seems to be relatively similar to more advanced countries, the frequency is somewhat higher
reflecting the higher rate of inflation in Slovakia. Note that, despite lower rates of inflation, the
frequency for the U.S. data is similar to that for Slovakia. However, as emphasized recently by
Nakamura and Steinsson (2007), the frequency of price changes for the U.S. is about half lower,
when sales are excluded in the calculation of the frequency of price changes. In this case, the
attendant frequency falls to about 0.1, which is a value similar to the euro area. Sales thus seem to
Price Setting and Market Structure - 54 -
play more important role in the U.S. than in Europe, whereby the frequency is around 0.15
regardless whether sales are included or not.
Table 4 – Frequency of Price Changes, International Comparison
Slov
akia
Aus
tria
Belg
ium
Fran
ce
Italy
Luxe
mbo
urg
Net
herla
nds
Portu
gal
USA
Food and Non-Alcoholic Beverages 0.43 0.17 0.28 0.19 0.15 0.19 0.23 0.37 0.25Alcoholic Beverages and Tobacco 0.31 0.15 0.11 0.22 0.08 0.14 0.19 0.14 NAClothing and Footwear 0.25 0.12 0.03 0.18 0.05 0.20 0.21 0.27 0.29Housing, Water, Gas and Electricity 0.19 0.11 0.22 0.24 0.23 0.29 0.19 0.08 NAFurnishing & Maintenance of Housing 0.24 0.07 0.04 0.16 0.04 0.18 0.08 0.11 0.26Health Care Expenses NA 0.06 0.11 0.08 NA 0.03 NA 0.05 0.09Transport 0.59 0.36 0.21 0.36 0.28 0.21 0.88 0.26 0.39Communications NA 0.09 0.06 0.23 NA 0.04 NA 0.11 NALeisure and Culture 0.24 0.24 0.12 0.13 0.05 0.13 0.08 0.12 0.11Education NA 0.05 NA 0.06 NA 0.05 NA 0.08 NAHotels, Cafés and Restaurants 0.14 0.08 0.03 0.08 0.06 0.05 0.08 0.19 NAMiscellaneous Goods and Services 0.25 0.07 0.06 0.12 0.04 0.11 0.10 0.11 0.11 % of CPI 57 100 68 65 20 100 8 100 ---Total 0.34 0.15 0.17 0.19 0.09 0.17 0.17 0.22 0.26Average inflation rate, in %, yearly 9 2 2.2 1.5 2.5 2.5 2.9 2.6 2.4
Notes: Own calculations for Slovakia, the authors of results in other countries are as follows: Austria –Baumgartner et al. (2005), Belgium - Aucremanne and Dhyne (2004), France - Baudry et al. (2004), Italy – Veronese et al. (2005), Luxembourg – Lunnemann and Matha (2005b), Netherlands – Jonker et al. (2005), Portugal – Dias et al. (2004), USA – Bils and Klenow (2005). All averaged frequencies are expenditure-weighted. International comparison of the frequency of price changes in the Euro area countries based on 50 representative products is available in Dhyne et al. (2006).
2.3.2.3 Magnitude of Price Changes In this section, we estimate the average size of price increases and decreases. We find that the
magnitude of price changes is sizeable in both directions. The average size of expenditure-
weighted price increases and decreases is 12% and -11%, respectively. The corresponding size of
changes rises to 16% and -14% without CPI weights.
The results are comparable to the findings for other Euro area countries (see Dhyne et al., 2006),
which indicate that the magnitude of price changes is typically nearly 10%, both for price
Price Setting and Market Structure - 55 -
increases and decreases. In general, the larger size of price changes in Slovakia may indicate lower
degree of market competition, as compared to more developed markets in Western Europe.
Figure 4 - Size of Price Changes
0
40
80
120
160
200
-0.4 -0.2 0.0 0.2 0.4 0.6
Although most price increases and decreases range between 5-15% in absolute terms (274 and
303 products, respectively), there are very long tails toward one (see Figure 4).
Looking at the product groups, Table 5 shows that services exhibit the greatest magnitude of
price changes, at near 15% in absolute terms. On the other hand, non-durables, raw goods and
perishable products tend to display much smaller changes in prices. Durables and processed
goods stand somewhere in between.
Price Setting and Market Structure - 56 -
Table 5 - Size of Price Changes, Raw vs. Processed Goods, Perishables and Durables,
Non-Durables and Services
No. of products
Sample Weights
Increase – Weighted
Decrease - Weighted
Processed Goods 375 0.79 0.12 -0.12 Raw Goods 48 0.21 0.10 -0.08 Perishables 64 0.24 0.11 -0.10 Durables 231 0.36 0.13 -0.11 Non-durables 136 0.50 0.10 -0.10 Services 56 0.14 0.17 -0.15 Total 423 1 0.12 -0.11
Notes: See Table 2.
2.3.2.4 Price Dispersion
Price dispersion is one of the main characteristics of the market structure. While there is a variety
of theoretical models on price dispersion and market structure, recent empirical evidence suggest
that price dispersion decreases with market competition. The results of Caglayan et al. (2007)
show that price dispersion is lower in more competitive environment using micro level price data
from Turkey. Baye et al. (2004) analyze internet prices and find that price dispersion is greater
when smaller number of firms list their prices on internet price comparison site (this is also
found by Leiter and Warin (2007), who use different online shopping/price comparison site).
Similarly, Gerardi and Shapiro (2007) find a negative effect of competition on price dispersion,
with the effect being the most significant on the routes with more heterogeneous customer base.
Price Setting and Market Structure - 57 -
Figure 5 –Product-Specific Price Dispersion
0
10
20
30
40
50
60
70
0.0 0.1 0.2 0.3 0.4 0.5 0.6
We examine to what extent prices of identical products sold in the same month differ across
different stores. Figure 5 shows large cross-sectional variation in price dispersion. Table 6
suggests that prices in services sector are by far the most dispersed, in comparison to non-
durables, raw goods and perishables is twice as much. Interestingly, our results appear to be in
line with Crucini et al. (2005), who find, using micro data from the EU-15 that the price
dispersion decreases with tradability of the product.
Table 6 – Price Dispersion
No. of products
Sample Weights
Dispersion –Weighted
Dispersion –No weights
Processed Goods 375 0.79 0.130 0.161 Raw Goods 48 0.21 0.085 0.112 Perishables 64 0.24 0.081 0.100 Durables 231 0.36 0.155 0.172 Non-durables 136 0.50 0.078 0.094 Services 56 0.14 0.181 0.233 Total 423 1 0.121 0.155
Notes: See Table 2.
2.3.2.5 Trade-off between Frequency and Size of Price Changes? Menu costs models predict that when there are significant costs of price adjustment, price
adjustment should occur less frequently and the change should be sizeable (Mankiw, 1985). In
Price Setting and Market Structure - 58 -
this regard, Carlton (1986) finds a negative correlation between the frequency of price changes
and the average absolute price change. In principle, the correlation may differ according to
whether the price increases or decreases. We find that the simple correlation between product-
specific frequency and size of price increases is -0.03, but fails to be significant. On the other
hand, frequency the size of price decreases are negatively correlated, with a coefficient oft -0.17,
significant at 5% level. This would suggest some mild support for the notion of trade-off
between the frequency and the magnitude of price changes.
To examine this issue further, we fit a spline among the frequency, size of price increases and
decreases. Interestingly, the results in Figure 6 point to a negative relationship between the
frequency and size only for more rigid prices. For some components of the basket, the
relationship is even reversed. These are products changing prices often and by large amounts
(such as fruits and vegetables). There also seem to be products with convex costs of price
adjustment, in line with Rotemberg (1982) model. For instance, products such as gasoline change
prices often, but only by a tiny magnitude.
We next try to identify the factors that affect the firm’s price behavior, analyzing the
determinants of inflation persistence, frequency of price changes and price dispersion.
Price Setting and Market Structure - 59 -
Figure 6 – Frequency and Size of Price Changes
2.3.2.6 Determinants of Price Setting Behavior Table 7 presents the results on the determinants of inflation persistence. Price dispersion, as a
proxy for the level of market competition, tends to decrease persistence.36 This is in line with our
conjecture laid out in section 2; a more competitive environment is associated with greater
persistence of shocks. Furthermore, a greater frequency of price changes is associated with
smaller persistence. Galí (2004) emphasizes that this finding is likely to be a consequence of the
backward looking behavior of some fraction of price setters. Assuming a hybrid NKPC as in Gali
and Gertler (1999), he shows that inflation persistence is an increasing function of the fraction of
backward looking price setters. In this regard, Cecchetti and Debelle (2006) claim that greater
price rigidity generates more backward looking behavior and thus more persistence. It is worth
mentioning that here our results differ from Bils and Klenow (2004), who report a positive
correlation between the frequency of price changes and inflation persistence for their full sample
36 The list of our instrumental variables used in this section includes: raw goods, services, durables, perishables and expenditure weight. Note that the set of instruments differs across the tables and thus the degrees of freedom for Sargan-Hansen test varies correspondingly.
Price Setting and Market Structure - 60 -
(and statistically insignificant correlation for the short sample). In contrast, we find a statistically
significant correlation of -0.14, which gives some support to Calvo pricing model.
Table 7 – Determinants of Inflation Persistence
(1) (2) (3) (4) (5) Price dispersion -0.18*** -0.41*** (0.07) (0.12) Frequency 0.04 -0.17*** (0.04) (0.06) Raw goods -0.03** -0.04** (0.01) (0.02) Services -0.03** -0.04*** (0.01) (0.01) Durables -0.01 (0.01) Perishables -0.002 (0.01) No. of observations 423 423 423 423 423 Adj. R-squared --- --- --- 0.03 0.04 Sargan-Hansen test 7.97(0.09) 13.9(0.01) 1.17(0.56) --- --- Estimation Method GMM GMM GMM OLS OLS
Note: Heteroscedasticity corrected standard errors & covariance. ***, **, and * - denotes significance at 1 percent, 5 percent, and 10 percent, respectively.
Staggered price setting models predict that in the steady state the products with less frequent
price changes exhibit greater price dispersion and smaller product-specific inflation rate. The
price dispersion among the homogenous products may occur because of the inability of firms to
adjust instantaneously their price to a given shock. The greater the inability, the greater is price
dispersion. Higher inflation rates erode the real price more quickly and therefore price adjustment
is triggered more often. Baharad and Eden (2004) try to discriminate between staggered price
models and uncertain and sequential trade (UST) models by running a regression between the
frequency of price changes and price dispersion and inflation rate.37 In contrast to the UST
model, the staggered price model implies a significant relationship between frequency of price
37 UST models assume that there is price dispersion in the equilibrium. There is a trade-off between high price and the probability of making a sale in the model. If seller quotes relatively low price, he increases the changes of selling his product. Seller may also charge high price, but at the expense that the probability of making a sale is then rather low. This implies that the seller does not have to change the price of a product, even if the inflation erodes his price, because it increases the probability of making a sale. Therefore, UST models claim that prices may be seemingly rigid, to a certain extent.
Price Setting and Market Structure - 61 -
changes, price dispersion and inflation rate. In addition, we also examine the importance of
product characteristics in determining the length of inaction in price adjustment.
Table 8 contains the results of our analysis on the determinants of frequency of price changes.
The results suggest that product characteristics are the primary force triggering the price changes.
Individual inflation rate matters as well. Price dispersion seems to be associated with less frequent
price changes, but this result should be interpreted with caution, as the test for overidentifying
restrictions is rejected.38 We have also tested for the presence of any non-linear relationship
between frequency and inflation, but we failed to find any significant non-linearity. All in all, our
results give support to the staggered price setting model.
Table 8 – Determinants of Frequency of Price Changes
(1) (2) (3) (4) (5) Price dispersion -3.62*** -1.63*** (0.78) (0.21) Individual inflation 0.06** 0.02** (0.03) (0.01) Raw goods 0.26*** 0.17*** (0.03) (0.02) Services -0.10*** -0.17 *** (0.01) (0.02) Durables -0.10*** (0.01) Perishables 0.05*** (0.02) No. of observations 423 423 423 423 423 Adj. R-squared --- --- --- 0.47 0.59 Sargan-Hansen test 6.00(0.01) 2.25(0.13) 11.87(0.01) --- --- Estimation Method GMM GMM GMM OLS OLS
Note: Heteroscedasticity corrected standard errors & covariance. ***, **, and * - denotes significance at 1 percent, 5 percent, and 10 percent, respectively.
Table 9 contains our results on the determinants of price dispersion. We find that greater
frequency of price changes decreases the dispersion, while individual inflation increases it. The
38 The attendant OLS estimates, available upon request, show that a higher degree of competition is associated with less rigid prices, as found e.g. by Bils and Klenow (2004).
Price Setting and Market Structure - 62 -
latter finding is in line with large literature represented by e.g. Lach and Tsidon (1992) and
Konieczny and Szkrypacz (2005), who find that inflation adds to price variability, while the
former comply with Caglayan et al. (2007). In addition, prices in services exhibit greater
dispersion. On the other hand, prices of raw goods are typically less dispersed.
Table 9 – Determinants of Price Dispersion
(1) (2) (3) (4) (5) Frequency -0.50*** -0.33*** (0.05) (0.09) Individual inflation 0.02*** 0.02*** (0.01) (0.01) Raw goods -0.04*** 0.01 (0.01) (0.01) Services 0.09*** 0.15 *** (0.02) (0.02) Durables 0.08*** (0.01) Perishables 0.01 (0.01) No. of observations 423 423 423 423 423 Adj. R-squared --- --- --- 0.12 0.25 Sargan-Hansen test 25.6(0.00) 3.43(0.18) 1.20 (0.27) --- --- Estimation Method GMM GMM GMM OLS OLS
Note: Heteroscedasticity corrected standard errors & covariance. ***, **, and * - denotes significance at 1 percent, 5 percent, and 10 percent, respectively.
2.4 Conclusions The analysis on price setting for a large micro dataset based on actual prices indicates that
Slovakia shares many of the features found in micro studies for advanced market economies.
All in all, not only average inflation, but also the frequency of price changes and the persistence
in inflation appear to be higher in Slovakia as compared to advanced economies. The significantly
higher persistence in emerging economies that are in the process of joining the European
Monetary Union is a cause for concern for policy-makers in the Eurozone, as it would imply
asymmetric effects of the common monetary policy, and persistent disequilibria in real exchange
rates across members of the Eurozone. Looking forward, higher market competition brought
Price Setting and Market Structure - 63 -
about by the integration in the European Union and in the European Monetary Union are
unlikely to reduce such persistence, as our results indicate a positive correlation between market
competition and inflation persistence. By contrast, an increase in the importance of the service
sectors, that are at present compressed in emerging countries like Slovakia, may in fact reduce
persistence. Therefore, it is not easy to predict the tendency in persistence as Slovakia becomes
more and more integrated in the EU and its economic structure converges to that of EU
countries.
Price Setting and Market Structure - 64 -
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Price Setting and Market Structure - 69 -
APPENDIX 1 – PRICING STATISTICS
In this Appendix we derive formally the pricing statistics used in the paper. This includes the
frequency of price changes, duration of single price spell, the size of price increases/decreases,
and price dispersion (inflation persistence is already defined formally in the main text).
To define the frequency of price changes and duration of single price spell formally, let istp
denote the price of product i in store s at time t , where Tt ,...,1= , Ss ,...,1= and Ii ,...,1= .
Let
1
1
1
0
−
−
≠
=⎩⎨⎧=
istist
istist
pp
pp
if
ifistx
As a result, the product-specific frequency of price changes, iµ , is computed as:
∑ ∑= =
=T
t
S
sisti x
ST 1 1
11µ
We compute average duration of single price spell, φ , as simple average over product-specific
frequencies ∑=
−=I
iiI 1
11
1 µφ or weighted average of product-specific frequencies
1
1 12
1−
= =
⎟⎠
⎞⎜⎝
⎛= ∑∑
I
i
W
wiiwI
µφ , where iw is the consumption weight of product i in the basket (note
that ∑=
=W
wiw
11). Alternatively, the duration can be defined in terms of medians instead of
averages.
Price Setting and Market Structure - 70 -
The product-specific size of price increases, iλ , is computed as ∑ ∑= = −
−−=
T
t
S
s ist
ististi p
ppST 1 1 1
111λ ,
conditional on that 1−> istist pp . Similarly, the product-specific size of price decreases, iτ , is
computed as ∑ ∑= = −
−−=
T
t
S
s ist
ististi p
ppST 1 1 1
111τ , conditional on that 1−< istist pp . The average size of
price change can be computed as the simple or weighted average (or median) of product-specific
size of price changes.
Product-specific price dispersion is defined as follows. ( )∑=
=T
tijti pSD
T 1
log1σ , where ijtplog is a
logarithm of average price of product i at region j .
Price Setting and Market Structure - 71 -
THE RESULTS ON PRODUCT-SPECIFIC DESCRIPTIVE STATISTICS (all 423 products)
CPI W
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Freq
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Dur
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Price
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Infla
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rate
– y
-o-y
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Size
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crea
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Size
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crea
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Infla
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Rice 0.29 0.40 2.51 0.07 1.77 0.07 -0.07 0.88Rice in boiling packets 0.03 0.38 2.66 0.08 -9.98 0.15 -0.16 0.93Wheat Flour Half Fine 0.42 0.40 2.48 0.05 2.40 0.07 -0.08 0.95Farina 0.03 0.31 3.19 0.04 6.02 0.06 -0.07 0.85Rye bread 1.33 0.29 3.49 0.12 9.11 0.10 -0.10 0.88Bread white 0.80 0.25 3.97 0.13 8.70 0.10 -0.13 0.85White roll 1.02 0.22 4.63 0.09 5.03 0.13 -0.14 0.90Christmas cake 0.61 0.29 3.43 0.13 8.82 0.10 -0.12 0.85Biscuits without filling 0.17 0.35 2.88 0.10 9.38 0.09 -0.16 0.95Biscuits with filling 0.33 0.36 2.79 0.07 7.30 0.05 -0.07 0.90Waffles with filling 0.59 0.36 2.79 0.06 6.09 0.06 -0.07 0.90Wafers without flavor 0.21 0.39 2.54 0.06 7.23 0.08 -0.10 0.90Salted crackers 0.16 0.32 3.15 0.19 9.43 0.14 -0.14 0.90Pasta (with eggs) 0.42 0.38 2.63 0.13 6.64 0.09 -0.09 0.90Dumpling 0.31 0.30 3.33 0.11 7.24 0.08 -0.10 0.90Puff pastry (listkove) 0.10 0.36 2.76 0.07 4.85 0.11 -0.12 0.88Porridge (without flavor) 0.03 0.39 2.58 0.08 2.10 0.10 -0.10 0.80Chuck roast with bone 0.23 0.37 2.69 0.08 2.66 0.09 -0.09 0.90Boneless chuck roast 0.24 0.39 2.58 0.06 5.78 0.07 -0.08 0.85Beef rear without bone 0.36 0.40 2.50 0.06 4.33 0.06 -0.07 0.80Beef joint without bone, lower 0.09 0.32 3.13 0.06 4.80 0.07 -0.08 0.80Pork meat with bone 0.93 0.57 1.75 0.04 4.83 0.06 -0.06 0.73Pork neck with bone 0.43 0.54 1.84 0.04 5.04 0.07 -0.06 0.83Flank of bacon 0.29 0.54 1.84 0.05 5.96 0.11 -0.10 0.93Pork leg without bone 0.36 0.57 1.76 0.06 3.76 0.09 -0.08 0.83Pork shoulder without bone 0.47 0.56 1.78 0.05 4.34 0.07 -0.06 0.88Chicken without insides 1.16 0.53 1.90 0.03 6.15 0.05 -0.06 0.88Chicken portioned fresh and frozen 1.19 0.49 2.02 0.04 2.99 0.08 -0.08 0.90Pork diet salami 0.28 0.46 2.17 0.06 0.81 0.08 -0.08 0.90Fine frankfurters 0.61 0.47 2.13 0.05 1.62 0.07 -0.07 0.93Ham salami 0.52 0.47 2.12 0.04 0.90 0.06 -0.06 0.93Durable salami 0.59 0.38 2.62 0.06 4.50 0.06 -0.08 0.80Boneless smoked pork neck 0.16 0.58 1.73 0.06 7.39 0.13 -0.12 0.90Pork stewed ham 0.59 0.44 2.30 0.04 2.44 0.06 -0.06 0.93Smoked bacon with skin 0.26 0.37 2.69 0.13 10.20 0.16 -0.17 0.90Luncheon meat pork 0.03 0.35 2.86 0.10 1.78 0.11 -0.11 0.88Pork meat paste 0.55 0.40 2.48 0.12 3.40 0.11 -0.10 0.93Pork liver 0.10 0.33 3.05 0.08 0.78 0.09 -0.09 0.93File-fish not breaded 0.43 0.45 2.24 0.07 12.90 0.16 -0.17 0.93Carp (live and frozen) 0.07 0.22 4.51 0.28 4.18 0.16 -0.13 0.93
Price Setting and Market Structure - 72 -
Smoked fish (mackerel with head) 0.05 0.33 3.05 0.08 10.94 0.08 -0.09 0.93Sardines in oil 0.24 0.38 2.60 0.07 6.82 0.07 -0.07 0.95Fish salad with mayonnaise 0.26 0.35 2.89 0.12 8.64 0.10 -0.14 0.88Fish in sour 0.09 0.36 2.78 0.08 10.11 0.13 -0.17 0.93Pasteurized half-fat milk 0.90 0.30 3.29 0.07 6.99 0.05 -0.05 0.88Thick milk Tatra without sugar 0.02 0.33 3.00 0.04 11.14 0.08 -0.13 0.83Dried Milk for Babies 0.09 0.39 2.60 0.06 6.15 0.15 -0.13 0.93Dried Milk Half-fat 0.02 0.41 2.45 0.06 3.87 0.10 -0.10 0.93Fruit yoghurt 1.02 0.39 2.54 0.07 -0.10 0.06 -0.08 0.95Sour milk (acidophilus) 0.16 0.27 3.74 0.24 79.07 0.40 -0.14 0.95Sweet cream 33% 0.24 0.31 3.25 0.05 5.86 0.05 -0.05 0.90Sour cream 0.29 0.32 3.16 0.07 6.48 0.08 -0.11 0.78Cheese Eidam, block 0.74 0.39 2.53 0.05 5.87 0.05 -0.06 0.88Spreadable cheese 0.68 0.39 2.60 0.07 -9.23 0.07 -0.11 0.95Ostiepok rolled smoked cheese 0.03 0.39 2.59 0.10 7.94 0.07 -0.08 0.83Mold cheese Niva 0.09 0.34 2.93 0.06 7.81 0.08 -0.11 0.98Bryndza sheep cheese 0.14 0.50 2.01 0.05 7.22 0.06 -0.08 0.87 Eggs (chicken, fresh) 0.80 0.65 1.54 0.07 4.81 0.11 -0.10 0.90Butter 0.68 0.45 2.20 0.05 -0.47 0.06 -0.06 0.93Margarine 0.66 0.38 2.60 0.08 4.15 0.07 -0.08 0.95Edible oil 0.85 0.39 2.56 0.05 4.60 0.06 -0.06 0.95Pork lard 0.03 0.50 1.98 0.14 5.05 0.16 -0.14 0.93Apples 0.59 0.77 1.30 0.11 6.45 0.17 -0.16 0.85Oranges 0.28 0.81 1.23 0.08 6.57 0.16 -0.12 0.90Mandarins 0.24 0.79 1.27 0.10 3.91 0.18 -0.14 0.85Lemons 0.12 0.77 1.29 0.07 2.27 0.12 -0.10 0.78Kiwi 0.07 0.77 1.30 0.17 6.11 0.33 -0.21 0.85Bananas 0.55 0.83 1.21 0.06 7.87 0.16 -0.13 0.54Dried grapes 0.05 0.42 2.39 0.07 6.41 0.11 -0.11 0.90Peanuts peeled salted 0.29 0.41 2.45 0.09 -0.97 0.10 -0.10 0.93Poppy seeds 0.05 0.39 2.54 0.11 6.04 0.17 -0.14 0.93Celery 0.02 0.61 1.64 0.17 5.91 0.31 -0.22 0.68Carrot 0.07 0.72 1.40 0.16 9.38 0.31 -0.22 0.78Parsley 0.03 0.75 1.33 0.17 19.20 0.37 -0.23 0.88Cauliflower 0.10 0.78 1.28 0.18 4.28 0.38 -0.23 0.51Cabbage (white) 0.10 0.66 1.51 0.15 4.64 0.33 -0.22 0.76Salad cucumbers 0.14 0.92 1.09 0.15 4.14 0.72 -0.33 0.71Paprika 0.21 0.90 1.11 0.18 5.64 0.52 -0.29 0.78Onion 0.10 0.68 1.47 0.12 4.59 0.24 -0.19 0.88Tomatoes 0.28 0.67 1.50 0.41 5.37 0.44 -0.28 0.63Beans white dried 0.03 0.36 2.75 0.10 3.70 0.09 -0.09 0.95Lentils (big) 0.03 0.35 2.85 0.08 3.54 0.08 -0.09 0.90Frozen vegetable mix 0.19 0.38 2.62 0.13 9.97 0.12 -0.13 0.85Frozen spinach 0.03 0.38 2.62 0.08 2.83 0.10 -0.10 0.93Sour cabbage (sterilized) 0.07 0.39 2.57 0.07 4.73 0.11 -0.10 0.90Sterilized peas in salty water 0.07 0.33 3.00 0.12 4.22 0.15 -0.14 0.93Paprika and tomatoes sterilized (without sausage) 0.02 0.48 2.10 0.07 -4.43 0.17 -0.19 0.88Potatoes 0.40 0.67 1.49 0.14 7.66 0.40 -0.20 0.78Potato chips 0.16 0.39 2.53 0.08 8.75 0.09 -0.09 0.93Frozen French fries 0.10 0.45 2.24 0.11 0.04 0.11 -0.11 0.95Crystal sugar 0.61 0.42 2.39 0.04 8.23 0.10 -0.08 0.88Ground sugar 0.14 0.42 2.37 0.05 9.17 0.11 -0.10 0.93Honey 0.12 0.46 2.15 0.10 1.92 0.11 -0.11 0.95Strawberry jam 0.03 0.32 3.11 0.06 3.02 0.07 -0.08 0.93
Price Setting and Market Structure - 73 -
Milk chocolate 0.45 0.39 2.54 0.08 4.32 0.06 -0.07 0.83Cooking chocolate 0.12 0.37 2.68 0.05 3.96 0.08 -0.10 0.83Chocolate bar with filling 0.26 0.34 2.97 0.09 6.77 0.09 -0.12 0.90Dessert chocolates 0.29 0.40 2.48 0.10 8.31 0.07 -0.07 0.83Fruit jelly 0.07 0.34 2.90 0.11 7.77 0.09 -0.10 0.88Hard candies without filling 0.31 0.39 2.57 0.08 10.04 0.07 -0.07 0.90Chewing gum - slices 0.42 0.23 4.37 0.18 -2.47 0.11 -0.12 0.93Salt 0.07 0.25 3.94 0.04 5.28 0.05 -0.06 0.85Ground sweet paprika 0.09 0.38 2.62 0.10 6.70 0.13 -0.11 0.88Ground pepper 0.07 0.40 2.50 0.11 18.51 0.18 -0.13 0.85Caraway not ground 0.02 0.34 2.90 0.15 4.34 0.20 -0.15 0.78Vinegar 8% 0.10 0.34 2.94 0.06 1.82 0.10 -0.10 0.93Mustard full-fat 0.17 0.32 3.08 0.06 3.77 0.07 -0.08 0.76Ketchup 0.21 0.44 2.26 0.10 1.23 0.10 -0.10 0.83Mayonnaise 0.10 0.39 2.57 0.10 39.82 0.19 -0.09 0.95Baking powder 0.03 0.26 3.81 0.06 -2.52 0.16 -0.15 0.90Fresh yeast 0.07 0.27 3.72 0.31 11.92 0.21 -0.14 0.95Dehydrated soup (not instant) 0.19 0.39 2.56 0.17 2.49 0.10 -0.10 0.85Vanilla pudding 0.07 0.31 3.18 0.07 9.00 0.10 -0.09 0.88Dried vegetable flavoring 0.21 0.35 2.86 0.05 4.14 0.08 -0.07 0.85Coffee beans 0.90 0.43 2.33 0.08 -0.98 0.09 -0.09 0.95Instant coffee with caffeine 0.31 0.36 2.79 0.09 5.69 0.08 -0.09 0.85Black tea without flavor 0.28 0.32 3.17 0.17 3.36 0.12 -0.13 0.80Cocoa powder 0.05 0.34 2.90 0.05 0.38 0.09 -0.09 0.80Cocoa granko 0.12 0.36 2.81 0.04 5.04 0.06 -0.06 0.83Table mineral water 0.66 0.29 3.41 0.11 7.42 0.11 -0.12 0.93Fruit syrup 0.33 0.31 3.20 0.07 -0.04 0.06 -0.06 0.95Rum 38-40% 0.36 0.36 2.77 0.03 2.27 0.05 -0.06 0.95Vodka 38-40% 1.56 0.33 3.01 0.09 -4.92 0.07 -0.09 0.95Brandy 38-40% 1.26 0.37 2.71 0.08 -7.99 0.06 -0.11 0.95Bottled red wine 0.38 0.36 2.79 0.04 4.30 0.05 -0.05 0.90Bottled white wine 0.88 0.36 2.80 0.05 3.20 0.05 -0.05 0.85Bottled sparkling wine 0.68 0.31 3.18 0.03 3.49 0.05 -0.05 0.88Beer 10% bottled 1.11 0.28 3.57 0.09 7.01 0.08 -0.08 0.85Beer 12% bottled 0.78 0.29 3.45 0.09 6.20 0.07 -0.07 0.85Cigarettes "Mars" 20 pieces 4.41 0.30 3.31 0.03 10.94 0.07 -0.06 0.85Cigarettes "Dalila" 20 pieces 0.62 0.22 4.61 0.04 12.88 0.12 -0.24 0.88Cotton dress for women 0.16 0.26 3.78 0.17 16.00 0.26 -0.20 0.90Synthetic dress material 0.05 0.29 3.42 0.17 14.42 0.26 -0.19 0.95Wool dress for women 0.02 0.18 5.52 0.12 -0.20 0.12 -0.10 0.76Short underwear for men 0.07 0.35 2.85 0.20 8.55 0.13 -0.10 0.83Long knitted underwear for men 0.02 0.39 2.55 0.13 1.47 0.12 -0.11 0.76Undershirt for men 0.03 0.32 3.15 0.13 2.62 0.13 -0.11 0.80Pajamas for men (from fabric) 0.09 0.32 3.13 0.15 5.14 0.14 -0.12 0.76Shorts for men 0.03 0.25 3.96 0.14 3.74 0.14 -0.12 0.90Bathrobe for men 0.02 0.18 5.53 0.21 5.75 0.23 -0.19 0.66Panties for women 0.16 0.33 3.00 0.18 10.29 0.13 -0.12 0.88Women night-gown 0.03 0.37 2.72 0.16 4.18 0.13 -0.11 0.88Women slip 0.00 0.29 3.40 0.14 8.06 0.13 -0.11 0.95Pajamas for women (from fabric) 0.09 0.30 3.30 0.14 5.59 0.16 -0.14 0.88Ladies bra 0.23 0.34 2.95 0.18 9.72 0.12 -0.11 0.85Home dress for women 0.02 0.27 3.74 0.26 4.69 0.18 -0.14 0.73Shirt for babies (from fabric) 0.00 0.24 4.11 0.10 6.51 0.12 -0.11 0.66Cotton napkins for babies tetra 0.00 0.29 3.47 0.05 5.04 0.09 -0.10 0.93
Price Setting and Market Structure - 74 -
Short sleeved shirt for babies 0.14 0.27 3.70 0.32 13.12 0.19 -0.16 0.83Panties for girls 0.02 0.30 3.31 0.12 4.31 0.12 -0.12 0.76Underwear for boys 0.02 0.31 3.23 0.16 2.57 0.13 -0.12 0.78Children pajamas 0.07 0.35 2.87 0.14 5.47 0.13 -0.12 0.85Shirt for babies without sleeves 0.02 0.27 3.69 0.13 4.77 0.12 -0.11 0.90Shorts for boys 0.00 0.18 5.60 0.18 8.72 0.29 -0.20 0.93Long winter coat for men 0.07 0.19 5.34 0.10 4.97 0.14 -0.13 0.93Winter jacket for men 0.31 0.24 4.16 0.18 4.72 0.17 -0.14 0.88Longer leather jacket for men 0.12 0.24 4.16 0.15 1.23 0.16 -0.13 0.83Spring jacket for men 0.09 0.18 5.56 0.15 4.72 0.21 -0.17 0.83Short sleeved shirt for men 0.42 0.24 4.12 0.22 1.30 0.14 -0.12 0.83Long winter coat for women 0.42 0.22 4.65 0.12 4.75 0.13 -0.12 0.93Winter jacket for women 0.24 0.20 5.03 0.21 4.88 0.17 -0.14 0.88Rabbit fur coat for women 0.09 0.18 5.46 0.13 3.00 0.13 -0.14 0.78Long spring coat for women 0.12 0.22 4.50 0.11 4.10 0.15 -0.12 0.95Thin costume for women 0.57 0.31 3.22 0.13 5.19 0.16 -0.14 0.88Summer dress for women 0.24 0.16 6.30 0.15 1.19 0.22 -0.18 0.93Tailoring of a dress for women 0.05 0.11 8.94 0.34 6.67 0.20 -0.24 0.90Spring children jacket 0.07 0.21 4.73 0.31 0.55 0.28 -0.21 0.88Winter jacket for boys 0.35 0.21 4.78 0.21 -4.23 0.21 -0.15 0.88Jeans for boys 0.40 0.19 5.40 0.36 12.59 0.18 -0.14 0.86Small baby coat 0.02 0.31 3.28 0.19 4.94 0.15 -0.13 0.88Baby stockings 0.02 0.31 3.23 0.20 3.69 0.14 -0.12 0.83Stockings for women 0.24 0.23 4.43 0.26 2.89 0.10 -0.09 0.93Stocking for children 0.07 0.28 3.61 0.12 0.11 0.14 -0.14 0.90Handkerchief for women 0.00 0.25 4.02 0.12 4.02 0.13 -0.12 0.88Shawl for adults 0.03 0.16 6.43 0.13 6.55 0.20 -0.17 0.93Felt hat for men 0.00 0.21 4.87 0.21 1.13 0.18 -0.17 0.93Fur cap for women 0.00 0.20 5.09 0.17 7.98 0.22 -0.17 0.90Knit cap for children 0.05 0.18 5.41 0.25 2.68 0.16 -0.14 0.83Knit gloves for children 0.02 0.21 4.78 0.17 6.33 0.21 -0.20 0.88Tie for men 0.05 0.23 4.30 0.22 4.93 0.13 -0.12 0.88Thread for sewing, Tebex 0.02 0.23 4.33 0.19 5.48 0.09 -0.09 0.80Imitation of sewing silk (Nora) 0.00 0.13 7.89 0.12 -3.43 0.12 -0.11 0.90Knit tread 0.05 0.21 4.76 0.14 -19.86 0.12 -0.13 0.95Elastic waistband 0.00 0.19 5.26 0.10 5.41 0.21 -0.18 0.95Metal zipper 0.02 0.18 5.41 0.15 6.43 0.12 -0.11 0.90Cleaning of trousers in 3 days 0.02 0.10 9.92 0.08 4.98 0.21 -0.16 0.90Cleaning of coats 0.03 0.12 8.42 0.37 11.94 0.15 -0.15 0.93Leather walking shoes for men 0.38 0.35 2.85 0.25 4.43 0.10 -0.10 0.88Leather walking shoes for men, sandals 0.07 0.22 4.63 0.11 8.87 0.24 -0.18 0.90Leather winter shoes 0.23 0.19 5.19 0.18 11.47 0.15 -0.13 0.93Leather sport shoes 0.38 0.29 3.51 0.11 7.92 0.21 -0.16 0.83Leather walking shoes for women 0.55 0.28 3.60 0.20 5.14 0.14 -0.12 0.71Leather shoes for women, sandals 0.36 0.23 4.27 0.16 11.12 0.19 -0.16 0.93Leather winter shoes for women 0.52 0.21 4.66 0.17 8.80 0.16 -0.13 0.85Textile indoor shoes for women, slippers 0.09 0.31 3.24 0.14 7.02 0.14 -0.12 0.88Baby leather shoes 0.00 0.24 4.11 0.11 4.85 0.19 -0.16 0.95Plastic winter shoes for children - boots 0.24 0.23 4.27 0.13 5.67 0.08 -0.09 0.73Leather summer shoes for children, sandals 0.10 0.23 4.36 0.08 5.68 0.21 -0.16 0.93Women's shoes heels repair 0.05 0.28 3.60 0.15 3.47 0.19 -0.19 0.90Paint (Primalex, Farmal, Permal etc.) 0.19 0.25 4.06 0.21 9.00 0.17 -0.14 0.90Basic synthetic paint 0.10 0.23 4.36 0.08 9.05 0.11 -0.11 0.93Synthetic and oil paint thinner 0.03 0.22 4.47 0.15 4.51 0.12 -0.15 0.85
Price Setting and Market Structure - 75 -
Cement 0.57 0.22 4.52 0.07 10.10 0.09 -0.18 0.93Lime 0.05 0.18 5.66 0.06 9.91 0.09 -0.12 0.88Ceramic tiles, smooth, natural 0.64 0.22 4.59 0.09 7.94 0.14 -0.14 0.95Porous white and colored wall tiles 0.36 0.20 5.12 0.16 12.31 0.14 -0.13 0.78Wood (imitation of wood) board 0.23 0.10 9.88 0.16 6.94 0.12 -0.13 0.76Lever faucet 0.31 0.22 4.51 0.37 4.96 0.11 -0.10 0.90WC bowl with flusher 0.14 0.24 4.17 0.16 1.42 0.09 -0.08 0.85Installation services 0.21 0.13 7.97 0.09 5.44 0.15 -0.17 0.90Painting services 0.47 0.12 8.13 0.29 2.26 0.18 -0.18 0.95Varnishing of doors and windows 0.14 0.14 7.36 0.41 15.09 0.15 -0.16 0.90Glass services 0.12 0.16 6.31 0.26 11.36 0.15 -0.23 0.78Upholstered chair 0.14 0.23 4.36 0.19 5.78 0.12 -0.11 0.93Kitchen table 0.05 0.21 4.71 0.25 6.07 0.22 -0.21 0.71Two door closet for clothes 0.16 0.16 6.30 0.13 0.77 0.09 -0.09 0.88Kitchen cupboard 0.50 0.21 4.72 0.18 2.46 0.11 -0.11 0.73Bed with storage 0.12 0.15 6.62 0.11 -1.47 0.09 -0.09 0.93Bed for children with mattress 0.00 0.22 4.46 0.11 3.90 0.08 -0.08 0.90Furniture set for living room 0.33 0.21 4.87 0.18 -0.23 0.18 -0.15 0.93Upholstered set 0.54 0.24 4.17 0.10 1.44 0.11 -0.11 0.85Set of plastic garden furniture 0.03 0.20 5.02 0.22 -2.37 0.10 -0.10 0.95Synthetic carpet sewn-in 0.14 0.25 3.95 0.19 2.38 0.12 -0.10 0.83Plastic floor covering (pvc) 0.10 0.27 3.76 0.15 -1.96 0.16 -0.20 0.93Repair of upholstered sitting set 0.05 0.10 9.75 0.34 -2.76 0.34 -0.24 0.95Curtains 0.16 0.21 4.78 0.28 -9.76 0.12 -0.11 0.93Bed sheet 0.03 0.16 6.07 0.17 4.68 0.10 -0.09 0.76Bed linen for children - 1 bed 0.00 0.23 4.37 0.08 1.60 0.13 -0.12 0.90Bed linen for adults - 1 bed damask 0.02 0.27 3.66 0.12 3.08 0.15 -0.13 0.73Bed linen for adults - 1 bed 0.09 0.32 3.16 0.16 3.10 0.09 -0.09 0.80Turkish towel 0.05 0.31 3.27 0.10 2.82 0.15 -0.16 0.90Table cloth 0.07 0.28 3.54 0.15 5.87 0.13 -0.10 0.78Dish cloth 0.02 0.21 4.65 0.09 2.41 0.11 -0.10 0.66Big synthetic blanket (Larisa) 0.03 0.23 4.42 0.09 4.21 0.10 -0.10 0.93Comforter filled with synthetic material 0.07 0.19 5.17 0.12 2.73 0.11 -0.12 0.93Down comforter; quilt feather filling 0.00 0.18 5.62 0.09 -5.22 0.09 -0.13 0.93Refrigerator with freezer 260 liter 0.23 0.32 3.11 0.08 0.68 0.09 -0.09 0.88Freezer 130 liter 0.02 0.19 5.23 0.04 1.13 0.07 -0.07 0.88Air damper 0.02 0.23 4.27 0.33 -15.92 0.30 -0.29 0.93Electric suitcase sewing machine 0.05 0.35 2.84 0.14 1.19 0.19 -0.17 0.93Electric kitchen robot 0.03 0.22 4.50 0.20 1.40 0.13 -0.12 0.93Electric hand whipping tool 0.03 0.23 4.44 0.09 3.62 0.10 -0.09 0.93Electric juicer 0.02 0.19 5.26 0.18 -0.32 0.12 -0.11 0.78Electric fryer 0.02 0.21 4.70 0.13 0.01 0.15 -0.13 0.88Electric coffee maker with filter 0.02 0.25 4.04 0.15 2.10 0.09 -0.09 0.90Repair of electric refrigerator 0.03 0.23 4.33 0.21 5.09 0.12 -0.11 0.83Repair of automatic washing machine 0.05 0.17 5.99 0.20 5.20 0.12 -0.15 0.66Repair of electric vacuum cleaner 0.02 0.15 6.58 0.30 18.88 0.25 -0.26 0.90Repair of combined stove 0.00 0.09 11.69 0.35 6.08 0.13 -0.14 0.80Glass without holder 100ml 0.09 0.30 3.34 0.22 2.60 0.13 -0.14 0.90Lead crystal cup with holder 0.05 0.28 3.59 0.16 2.91 0.13 -0.12 0.63Plate set for 6 persons 0.12 0.25 3.99 0.15 7.24 0.12 -0.12 0.95Porcelain cup with decorations 0.05 0.25 4.02 0.11 5.59 0.10 -0.09 0.93Storage cans Omnia 720ml 0.02 0.21 4.71 0.08 5.42 0.15 -0.13 0.80Glass bowl from silex with cover 0.02 0.29 3.48 0.10 8.38 0.12 -0.13 0.93Kitchen pot 4 liters 0.07 0.30 3.35 0.26 12.21 0.14 -0.11 0.95
Price Setting and Market Structure - 76 -
Enameled tea kettle 0.02 0.27 3.68 0.10 5.25 0.14 -0.13 0.85Cutlery for 6 persons - rustles 0.02 0.30 3.34 0.22 -0.52 0.17 -0.14 0.93Kitchen knife with plastic handle 0.03 0.27 3.77 0.20 2.95 0.11 -0.11 0.83Soup ladle - rustles 0.00 0.21 4.66 0.11 4.42 0.12 -0.11 0.95Pan without cover - tefal 0.09 0.30 3.39 0.16 4.26 0.14 -0.13 0.95Plastic bottle for babies 0.02 0.29 3.43 0.17 3.56 0.19 -0.15 0.90Kitchen scales - 1 bowl 0.03 0.22 4.54 0.07 7.69 0.13 -0.16 0.95Wooden ladle 0.03 0.23 4.42 0.13 4.39 0.14 -0.12 0.83Vacuum bottle without pump, 1 liter 0.09 0.28 3.61 0.08 5.08 0.10 -0.09 0.95Electric drilling machine - two speeds 0.10 0.24 4.13 0.14 2.98 0.13 -0.12 0.76Flat light switch 0.03 0.25 4.05 0.11 6.42 0.13 -0.16 0.93Electric adapter 0.02 0.20 5.03 0.07 2.94 0.12 -0.12 0.83Thin battery 1,5 v (alkaline) 0.17 0.25 3.99 0.13 5.02 0.12 -0.12 0.90Regular light bulb 60w 0.19 0.22 4.65 0.07 0.04 0.10 -0.09 0.93Tape measure, 2 meters 0.00 0.26 3.89 0.28 -5.32 0.24 -0.18 0.90Combination pliers (PVC handle) 0.00 0.23 4.30 0.26 5.20 0.28 -0.22 0.90Screw driver (PVC handle) 0.02 0.24 4.13 0.17 16.47 0.14 -0.12 0.95Metal rake without handle 0.00 0.19 5.27 0.09 2.75 0.14 -0.11 0.83Aluminum double ladder to 180cm 0.03 0.28 3.60 0.16 4.02 0.11 -0.10 0.85Household scissors 0.00 0.23 4.34 0.28 -0.85 0.34 -0.23 0.83Clothes drying rack 0.03 0.21 4.79 0.14 -1.92 0.09 -0.09 0.76Ironing board, holder and Teflon cover 0.02 0.24 4.23 0.19 1.18 0.22 -0.18 0.85Liquid detergent for dish-washing 0.19 0.28 3.54 0.08 5.14 0.10 -0.10 0.88Spray insecticide 0.02 0.24 4.18 0.07 7.18 0.08 -0.08 0.95Construction nails 70 mm 0.02 0.17 5.82 0.30 -5.29 0.10 -0.12 0.95Long screws 3x20mm 0.02 0.14 7.38 0.07 3.71 0.43 -0.23 0.95Mechanical carpet cleaning 0.03 0.15 6.66 0.34 6.48 0.17 -0.20 0.93Škoda Fabia 1,4 Classic (44 kW) 0.47 0.43 2.30 0.08 1.87 0.12 -0.10 0.90Škoda Felicia, 1999 0.17 0.45 2.23 0.16 3.28 0.20 -0.16 0.93Children bicycle 0.10 0.21 4.72 0.13 9.05 0.08 -0.08 0.90Radial car tire 0.57 0.28 3.59 0.10 2.29 0.31 -0.22 0.90Accumulator 0.17 0.44 2.27 0.26 7.53 0.28 -0.19 0.88Left front fender 0.05 0.30 3.32 0.22 5.32 0.17 -0.15 0.90Oil filter 0.03 0.31 3.20 0.38 4.07 0.45 -0.31 0.83Gasoline 91 octane 1.68 0.69 1.44 0.37 6.56 0.04 -0.03 0.90Gasoline 95 octane 5.02 0.69 1.45 0.01 10.35 0.04 -0.03 0.95Oil fuel 0.54 0.77 1.30 0.01 11.24 0.03 -0.03 0.95Motor oil 0.05 0.23 4.43 0.25 12.28 0.12 -0.19 0.90Gear box oil 0.00 0.22 4.52 0.22 12.77 0.10 -0.20 0.93Non freezing liquid for cooler 0.02 0.22 4.51 0.18 8.55 0.15 -0.13 0.88Complete repair of motor 0.38 0.14 6.95 0.47 6.54 0.57 -0.30 0.95Complete repair of brakes 0.09 0.24 4.17 0.61 12.85 0.40 -0.29 0.80Basic balancing of car wheels 0.02 0.20 4.98 0.46 5.68 0.34 -0.26 0.88Complete lacquer 0.24 0.11 8.96 0.32 11.13 0.16 -0.20 0.90Replacement of door frame 0.05 0.22 4.63 0.56 16.42 0.53 -0.31 0.80Car washing 0.05 0.12 8.65 0.24 7.17 0.21 -0.17 0.93Taxi - personal fare + fare for 5 km 0.03 0.15 6.89 0.19 9.37 0.16 -0.15 0.93Portable radio with tape, stereo 0.07 0.27 3.65 0.16 -4.89 0.22 -0.20 0.93Walkman 0.03 0.25 4.04 0.23 -4.07 0.26 -0.24 0.95Stereo set 0.36 0.25 4.03 0.14 10.84 0.16 -0.15 0.88TV set 0.54 0.23 4.30 0.10 -3.77 0.17 -0.12 0.93VCR - 6 heads 0.23 0.26 3.89 0.14 1.45 0.16 -0.11 0.90Camera with auto focus 0.05 0.22 4.49 0.20 1.43 0.19 -0.14 0.90Video camera 0.09 0.23 4.32 0.16 -3.05 0.17 -0.12 0.85
Price Setting and Market Structure - 77 -
PC, Pentium - without accessories 0.52 0.44 2.29 0.19 5.64 0.30 -0.18 0.88Electronic pocket calculator 0.03 0.21 4.66 0.31 -4.22 0.26 -0.18 0.90Compact disc 0.21 0.22 4.46 0.18 7.36 0.14 -0.13 0.93Videotape - clean 0.10 0.20 5.02 0.16 -6.61 0.12 -0.10 0.83Tape for sound recording - clean 0.03 0.21 4.70 0.28 -3.22 0.14 -0.15 0.83Color film into the camera 0.16 0.19 5.19 0.15 3.80 0.16 -0.14 0.90Teddy bear 50 cm 0.07 0.33 3.06 0.23 4.62 0.14 -0.12 0.90Dressed doll with hair, PVC, from 40-50cm 0.10 0.29 3.43 0.33 10.04 0.21 -0.16 0.85Small bicycle for children 0.10 0.20 5.06 0.24 12.46 0.38 -0.25 0.93Children game "Clovece, nehnevaj sa" 0.05 0.26 3.89 0.19 7.68 0.17 -0.16 0.85Paper puzzle 0.03 0.22 4.53 0.17 7.39 0.24 -0.16 0.76Construction set Duplo 0.14 0.30 3.37 0.46 6.05 0.30 -0.18 0.85Downhill skis, 140 - 160 cm 0.14 0.20 4.91 0.30 11.37 0.30 -0.21 0.90Binding for downhill skiing 0.03 0.18 5.64 0.15 -1.84 0.15 -0.13 0.93Plastic bob sled with brakes 0.02 0.15 6.74 0.12 3.45 0.10 -0.10 0.90Ice-skating shoes 0.12 0.20 4.92 0.09 2.69 0.11 -0.11 0.95Ball for volleyball 0.02 0.22 4.56 0.19 1.87 0.14 -0.13 0.83Sleeping pack with a pack 0.09 0.18 5.56 0.25 5.68 0.20 -0.16 0.88Rose bush 0.19 0.15 6.54 0.20 6.73 0.14 -0.12 0.90Apple tree 1st class 0.28 0.12 8.07 0.15 9.89 0.14 -0.12 0.93Fertilizer 0.03 0.26 3.89 0.17 7.65 0.15 -0.15 0.93Karafiat (a flower) 0.17 0.39 2.60 0.12 4.98 0.15 -0.13 0.71Rose 0.42 0.54 1.84 0.12 8.89 0.14 -0.11 0.76Dog food 0.35 0.33 3.04 0.15 -0.41 0.13 -0.17 0.90Covered swimming pool ticket 0.05 0.26 3.85 0.27 16.53 0.34 -0.27 0.85Fee for exercises (in fitness center) 0.07 0.12 8.66 0.31 12.06 0.26 -0.30 0.90Dancing course fee 0.12 0.05 22.00 0.33 11.11 0.25 -0.19 0.90Cinema ticket 0.09 0.26 3.87 0.12 14.41 0.11 -0.10 0.76Videotape - 1 day borrowing 0.03 0.08 12.05 0.15 7.55 0.23 -0.25 0.95ID picture 0.03 0.09 11.16 0.26 3.29 0.21 -0.18 0.93Color film developing 0.12 0.10 10.44 0.26 8.31 0.31 -0.23 0.93Colored photo enlargement 9x13 cm 0.14 0.09 11.28 0.11 -3.73 0.14 -0.12 0.85Books for children, age: from 6 to 9 0.09 0.20 5.10 0.15 8.07 0.12 -0.11 0.71Pocket Dictionary Slovak-English 0.02 0.17 5.93 0.27 4.86 0.17 -0.21 0.83Book - prose - foreign author 0.29 0.21 4.68 0.10 9.20 0.10 -0.09 0.71Book - prose - Slovak author 0.09 0.27 3.65 0.15 11.72 0.14 -0.15 0.85Colored postcard, in envelope 0.07 0.24 4.24 0.24 7.42 0.13 -0.12 0.90Spiral calendar, size 30x20cm 0.03 0.18 5.71 0.14 3.51 0.14 -0.13 0.93Notebook - half thick 40 sheets 0.23 0.19 5.24 0.07 4.54 0.08 -0.08 0.85Note book A4 format 0.02 0.28 3.58 0.15 2.61 0.14 -0.12 0.88Black pencil 0.02 0.22 4.47 0.15 5.18 0.13 -0.15 0.90Ball pen - medium content 0.07 0.19 5.16 0.16 -1.18 0.12 -0.11 0.85Celluloid ruler, 30 cm 0.02 0.14 6.99 0.13 -1.16 0.17 -0.18 0.95DESIGN - A4 0.02 0.16 6.16 0.12 2.76 0.21 -0.19 0.80Color pencils 0.05 0.23 4.32 0.12 3.71 0.11 -0.10 0.95Recreation in Slovakia for 7 days - hotel B* 0.71 0.27 3.76 0.19 7.44 0.26 -0.18 0.78Spain 14 days, airplane 1.63 0.15 6.89 0.17 11.92 0.15 -0.11 0.93Italy 7 nights, by bus 0.36 0.18 5.45 0.11 9.73 0.17 -0.17 0.93Trip to the neighboring country, within 500km, by coach
0.05 0.16 6.43 0.38 5.05 0.17 -0.20 0.90
Beef bouillon with meat and noodles 0.05 0.23 4.40 0.20 7.95 0.23 -0.18 0.85Beef goulash 0.02 0.29 3.39 0.15 8.29 0.15 -0.17 0.83Joint with ham and egg 0.03 0.26 3.83 0.18 4.06 0.24 -0.19 0.73Roasted pork meat 0.10 0.21 4.66 0.15 6.97 0.09 -0.14 0.95
Price Setting and Market Structure - 78 -
Fried pork meat (breadcrumbs) 0.19 0.26 3.82 0.14 5.45 0.16 -0.13 0.90Grilled or baked chicken 0.23 0.25 4.06 0.14 5.66 0.10 -0.15 0.88Pancakes with jam 0.03 0.34 2.97 0.20 9.90 0.19 -0.19 0.85Sheep cheese dumplings 0.03 0.21 4.83 0.14 7.42 0.11 -0.09 0.73Fried cheese 0.10 0.30 3.31 0.16 7.17 0.09 -0.12 0.80French fries 0.00 0.09 11.35 0.16 3.50 0.11 -0.10 0.88Dumplings, big 0.02 0.14 7.20 0.20 9.69 0.43 -0.26 0.73Stewed rice 0.02 0.16 6.10 0.25 15.80 0.21 -0.16 0.93Stewed cabbage 0.00 0.23 4.40 0.19 8.26 0.43 -0.28 0.80Cucumber salad 0.03 0.13 7.70 0.31 5.23 0.42 -0.30 0.88Caramel dessert "Veternik" 0.03 0.17 6.01 0.16 9.46 0.09 -0.11 0.85Ice cream 0.03 0.14 6.98 0.28 12.82 0.45 -0.34 0.83"Vlassky" salad 0.57 0.20 5.00 0.12 2.81 0.04 -0.04 0.95Sandwich with ham and vegetables 0.23 0.20 5.07 0.16 3.12 0.17 -0.13 0.88Coffee - 7 grams, 5 grams of sugar 0.10 0.19 5.27 0.16 8.57 0.31 -0.20 0.83Mineral water 0.02 0.18 5.43 0.34 12.94 0.46 -0.35 0.80Fruit soft drink 0.10 0.14 7.02 0.33 33.05 0.45 -0.30 0.95Cola soft drink 0.10 0.11 8.94 0.16 5.63 0.25 -0.19 0.68Beer 12%, light from barrel 0.09 0.16 6.28 0.16 7.33 0.16 -0.17 0.71Beer 12%, light bottled 0.24 0.12 8.68 0.20 6.80 0.25 -0.18 0.88White wine, bottled, domestic 0.03 0.13 7.76 0.14 4.67 0.26 -0.21 0.85Red wine, bottled, domestic 0.03 0.06 15.53 0.12 4.41 0.19 -0.18 0.85Dessert white wine, bottled, domestic 0.00 0.11 9.22 0.28 0.89 0.28 -0.18 0.76Slovak juniper brandy 40% 0.05 0.17 5.81 0.19 4.67 0.32 -0.27 0.85Brandy 40%, domestic production 0.02 0.15 6.52 0.25 4.86 0.34 -0.26 0.88Complete lunch in factory canteen 6.89 0.12 8.07 0.15 6.84 0.14 -0.14 0.71Electric hair dryer 0.02 0.21 4.81 0.20 4.03 0.28 -0.20 0.93Electric hair iron with accessories 0.02 0.20 4.93 0.32 2.98 0.27 -0.20 0.83Electric razor 0.05 0.24 4.23 0.21 0.80 0.22 -0.18 0.76Manual 2-blade metal shaving razor 0.03 0.22 4.65 0.23 13.33 0.19 -0.16 0.93Razor blade - 5 pieces n a pack 0.24 0.30 3.32 0.21 19.28 0.13 -0.10 0.95Shaving foam 0.09 0.27 3.67 0.12 9.35 0.11 -0.10 0.88Tooth brush 0.09 0.25 3.93 0.34 13.46 0.20 -0.18 0.80Tooth paste 0.54 0.27 3.67 0.17 5.19 0.12 -0.11 0.90Suntan milk with protective factor 0.07 0.19 5.32 0.23 81.34 0.74 -0.22 0.95Cosmetic alcohol Alpa 0.02 0.16 6.10 0.16 4.52 0.11 -0.12 0.85Body deodorant 0.35 0.29 3.51 0.40 7.53 0.15 -0.13 0.90Powder for children 0.00 0.24 4.24 0.05 7.36 0.08 -0.09 0.95Bath soap, higher quality 0.38 0.22 4.48 0.21 9.23 0.12 -0.10 0.95Folded bandage absorbent quality 0.05 0.31 3.18 0.09 4.52 0.09 -0.09 0.93Paper handkerchiefs (10 pieces) 0.16 0.22 4.60 0.11 -0.21 0.14 -0.12 0.85Disposable napkins for children 0.50 0.30 3.38 0.11 7.12 0.13 -0.11 0.95Sanitary napkins, 10 pcs in package 0.71 0.28 3.59 0.22 10.80 0.15 -0.13 0.95Toilet paper - 400 slips 0.62 0.20 5.07 0.05 -1.41 0.08 -0.08 0.66Hair shampoo 0.73 0.25 3.98 0.10 5.63 0.13 -0.11 0.95Golden wedding ring 0.54 0.19 5.16 0.11 0.95 0.11 -0.09 0.71Wrist watch for men 0.23 0.23 4.40 0.36 7.44 0.27 -0.18 0.83Alarm clock on battery 0.02 0.22 4.64 0.12 1.25 0.14 -0.13 0.85Wall clock of Quartz type 0.03 0.19 5.27 0.10 2.69 0.14 -0.15 0.93Golden chain - mechanically wrought 0.16 0.24 4.17 0.11 0.64 0.08 -0.08 0.83Complete repair of wrist watch for men 0.09 0.18 5.58 0.25 6.46 0.11 -0.10 0.80Leather bag for women 0.10 0.32 3.17 0.16 10.29 0.16 -0.12 0.80Plastic bag for women 0.19 0.34 2.97 0.15 5.17 0.11 -0.09 0.85Leather purse for women 0.07 0.28 3.58 0.20 11.98 0.14 -0.13 0.90
Price Setting and Market Structure - 79 -
Plastic suitcase 40x60cm 0.03 0.27 3.75 0.24 6.05 0.19 -0.17 0.93School bag 0.10 0.27 3.73 0.10 5.72 0.10 -0.10 0.90Children stroller combined 0.09 0.25 4.07 0.13 5.75 0.10 -0.11 0.90Matches 0.02 0.10 10.23 0.11 4.03 0.19 -0.15 0.88Sun glasses with ultraviolet filter 0.12 0.20 5.09 0.63 11.10 0.19 -0.14 0.85Umbrella for women 0.10 0.27 3.75 0.16 4.60 0.15 -0.18 0.80
Assessing Inflation Persistence - 80 -
3 Assessing Inflation Persistence: Micro Evidence on an Inflation
Targeting Economy*
3.1 Introduction
The sensitivity of aggregate inflation to various macroeconomic disturbances has been
traditionally at the focus of attention of monetary authorities. Indeed, the transmission of
monetary policy actions to prices depends on a number of factors, including inter alia the degree
of nominal rigidities. Consequently, in the last 20 years or so, there has been substantial research
investigating the macroeconomic consequences of nominal rigidities for the working of an
economy in response to various shocks and for the design of monetary policy rules. The result of
this effort has been a number of micro-founded models with price or wage stickiness which
predict various types of inflation dynamics. Nevertheless, two standard models in their original
versions, Calvo (1983) and Taylor (1980), imply no role for the backward-looking dimension of
inflation. These models, while assuming price stickiness, do not imply intrinsic inflation
stickiness.39
Several models address this issue by introducing the lagged value of inflation into a new
Keynesian Phillips curve. The rationale behind the inclusion of the lagged value differs across the * We thank Oxana Babetskaia, Martin Čihák, Oldřich Dědek, Tomáš Holub, Mario Holzner, Vladislav Flek, Michal Franta, Ondřej Kameník, Evžen Kočenda, Sangeeta Pratab, Kateřina Šmídková and the seminar participants at the Czech Economic Association annual conference, the European Economic Association annual congress, Deutsche Bundesbank, Charles University (Prague) and Czech National Bank for valuable comments. We are grateful to Robert Murárik for providing us with some of the data we used. All remaining errors are entirely our own. The views expressed in this paper are not necessarily those of the Czech National Bank or the European Bank for Reconstruction and Development. This research was supported by a grant from the CERGE-EI Foundation under a programme of the Global Development Network. All opinions are those of authors and have not been endorsed by CERGE-EI or the GDN. This paper has been supported by the Czech National Bank Research Project No. E5/05. 39 Assuming the Galí and Gertler (1999) hybrid New Keynesian Phillips curve specification for inflation dynamics, Angeloni et al. (2006) distinguish between various sources of inflation persistence and label them accordingly. They define intrinsic inflation persistence as the persistence originating in past inflation, extrinsic inflation persistence as the persistence related to inertia in the output gap, and expectation-based inflation persistence as the persistence rooted in deviations from rational expectations due, for example, to learning.
Assessing Inflation Persistence - 81 -
models. Apart from simply assuming rule of thumb behavior (Galí and Gertler, 1999), Fuhrer
and More (1995) suggest that the relative wage structure might be a reason for the backward-
looking nature of inflation. Mankiw and Reis (2002) stress the significance of information
processing lags in price setting mechanisms. In addition, Erceg and Levin (2003) and Orphanides
and Williams (2003) explain persistence with adaptive learning of agents in response to changes
in monetary policy regime. In consequence, the ability of monetary policy to anchor long-term
inflation expectations induces agents to rely on past inflation to a lesser extent. In this regard,
Sargent (1999) studies extensively the interactions between the conduct of monetary policy and
inflation persistence. Nimark (2005) suggests that optimal price setting with firm-specific
marginal cost rationalizes the link between past and current inflation. Calvo, Celasun and
Kumhof (2002) show that in an environment of high steady state inflation, firms not only choose
their price today, but also set the rate at which they will update prices in the future (the firm-
specific inflation rate). Under a monetary policy shock, some firms will not reset their inflation
rate (and prices) and this gives rise to inflation inertia.
Recent empirical research has shown that inflation persistence is generally much lower than
previously thought (e.g. Cecchetti and Debelle, 2006). This is mainly associated with two factors.
First, inflation persistence did indeed decline in the 1990s as compared to the 1970s and 1980s
(O’Reilly and Whelan, 2005). Second, greater care has been undertaken in econometric work.
Levin and Piger (2004) find that inflation persistence falls considerably when structural breaks are
accounted for. Next, stability of the monetary policy regime and central bank credibility help to
anchor long-run inflation expectations and reduce the extent of backward-looking behavior.
Levin et al. (2004) find that the adoption of an explicit inflation target40 significantly reduces the
extent to which economic agents use backward-looking information in terms of their inflation
forecasting and thus puts downward pressure on the persistence of inflation. 40 See Kotlán and Navrátil (2003) on the design of the inflation targeting regime in the Czech Republic, and Jonas and Mishkin (2003) on the inflation targeting experience of transition countries in general.
Assessing Inflation Persistence - 82 -
There are various reasons why it is vital to study inflation persistence at a disaggregated level.
Disaggregated analysis generally uncovers smaller inflation persistence across the
individual/sectoral price indexes compared to aggregate inflation. This suggests that inflation
persistence observed at the aggregate level may arise due to aggregation bias (see Granger, 1980,
and Zaffaroni, 2004) and due to the fact that idiosyncratic shocks will tend to disappear when a
substantial number of series are aggregated (Altissimo, Mojon and Zaffaroni, 2007). Disaggregate
analysis is also fruitful for understanding which components of various price indexes exhibit
greater inflation persistence. In addition, the role of structural breaks in estimating inflation
persistence can be tackled in a fuller manner.
Additionally, several studies have raised the issue of which factors lie behind the fact that the
inflation process is relatively persistent. Cournede et al. (2005) argue that the lower responsiveness
of aggregate inflation to output developments in the euro area in comparison to the U.S. is
caused by more rigid structural policy settings and relate it to trade barriers in the European
services sector. Analogously, European Commission (2003) points out that low competition in
services enhances the sector’s inflation inertia as measured at the aggregated level. On the other
hand, studies employing disaggregated data, such as Lunnemann and Matha (2005) for several
EU countries and Clark (2006) for the U.S., find little evidence that services display greater
inflation persistence than goods. Similarly, Coricelli and Horvath (2006) report results for
Slovakia indicating that inflation inertia in the services sector is even lower than for goods and
put forward an explanation of why (labor intensive) services, where the degree of competition is
typically lower as services are often not exposed to international competition, may in fact exhibit
smaller persistence. The argument is based on Calvo (2000), who shows that greater competition
in the market may actually slow down the adjustment to shocks, as the degree of strategic
Assessing Inflation Persistence - 83 -
complementarity increases with higher competition. All these aforementioned issues give further
impetus for individual or sectoral level analysis of inflation persistence.
One of the interesting applications of inflation persistence analysis at the disaggregate level is
provided by Cutler (2001). Cutler constructs an alternative measure of core inflation –
persistence-weighted core inflation. The measure is constructed in a way giving larger weights to
items exhibiting higher inflation persistence. Using UK data, Cutler finds that in terms of ability
to predict headline inflation this measure outperforms some other standard measures of core
inflation, such as those using a trimmed mean or weighted median or those excluding food and
energy prices.41
In addition, it is noteworthy that there is still very little evidence on price setting behavior in the
New EU Member States (NMSs). Typically, the few available studies focus on aggregate inflation
dynamics. More detailed evidence on price setting is provided by Ratfai (2006), who studies the
linkages between individual price dynamics and aggregate inflation with Hungarian data.
Additionally, Konieczny and Skrzypacz (2005) analyze the price dynamics of about 50 products
in Poland. Among other things, they show that more intense search is associated with smaller
price dispersion. Coricelli and Horvath (2006) give evidence on the empirical stylized features of
price setting behavior in Slovakia using a large micro-level dataset underlying the Slovak CPI.
Recently, inflation persistence at the aggregate level for the EU new members has also been
studied by Franta et al. (2007).
Therefore, a novel contribution of this study lies in exploring inflation persistence at the
disaggregate level in the Czech Republic using rich data collected by the Czech Statistical Office,
which cover about a thousand product categories over 1994–2005 (accounting also for structural 41 Notice that in general the forecasting ability of persistence-weighted measures of inflation may depend on the monetary regime and the degree of inflation persistence. For a discussion, see Smith (2004, 2005).
Assessing Inflation Persistence - 84 -
breaks). Furthermore, our study goes beyond a simple statistical description of the data and
makes an attempt to identify the determinants of inflation persistence. Of particular interest is the
examination of the so-called “services inflation persistence puzzle”, namely that more labor
intensive categories such as services often exhibit smaller persistence as compared to goods (see,
for example, Altissimo, Mojon and Zaffaroni, 2007; Clark, 2006; Coricelli and Horvath, 2006).
Finally, we construct “persistence-weighted” core inflation in line with Cutler (2001) and propose
a “persistence expenditure-weighted” core inflation measure that combines information on the
persistence of an individual product and its weight in the CPI basket, with the objective of
assessing its predictive performance (ability to capture inflation trends) compared to other
alternative approaches for core inflation measurement.
The paper is organized as follows. After this introduction to the subject and overview of the key
literature, the second section describes how inflation persistence is measured in practice,
formulates the research hypotheses and explains the estimation methodology. The third section
presents the data set used in the study. The fourth section provides the results. The last section
concludes and draws policy implications. An Appendix with additional results and sensitivity
checking follows.
3.2 Estimating inflation persistence
The literature generally applies two statistical approaches to estimating inflation persistence –
parametric and non-parametric. The parametric approach is more extensively applied in empirical
studies (Cecchetti and Debelle, 2006; Clark, 2006; Levin and Piger, 2004; Levin, Natalucci and
Piger, 2004). As advocated by Andrews and Chen (1994), the best scalar measure of persistence is
the sum of autoregressive coefficients in the dynamic equation for inflation:
επαπ µtjt
K
jjt
++=−
=∑
1
, (1)
Assessing Inflation Persistence - 85 -
where π t stands for the yearly inflation rate, µ and α j
are parameters, and ε t is the white-
noise disturbance. The lag length K is determined based on information criteria. Typically, ∑=
K
jj
1α
is interpreted as the measure of inflation persistence. Specification (1) may be labeled as naïve,
because it does not account for potential structural breaks. A number of recent studies apply
various tests for structural breaks (e.g. Cecchetti and Debelle, 2006; Levin and Piger, 2004).
A non-parametric approach has been recently put forward by Marquez (2004). This approach
builds on the idea that less persistent inflation is more likely to cross the long-run mean of the
inflation rate (or possibly the time-varying mean). Consequently, inflation persistence, ϕ , is
measured as Tn−= 1ϕ , where n is the number of times inflation crosses its equilibrium
value and T is the number of observations. Dias and Marquez (2005) derive the finite sample and
asymptotic properties of this non-parametric measure. They also conduct Monte Carlo
simulations and find that the bias of the estimate of persistence based on the non-parametric
approach is smaller for any sample size, as compared to the parametric measure from equation
(1). In addition, they argue that the non-parametric measure is more robust to structural breaks.
Nevertheless, the properties of this measure are investigated only for covariance stationary
processes.
Despite the potential attractiveness of the approaches described above, in our case we find that
most individual inflation rates follow an I(1) process (even if we control for structural breaks).
For such a case, the properties of the non-parametric approach have not been investigated yet.
Analogously, in the case of a parametric measure – e.g. the sum of autoregressive coefficients – it
is well known that non-stationarity of the variables would result in spurious regression.
Assessing Inflation Persistence - 86 -
Therefore, we do not report these measures and propose a different measure of the persistence
of inflation.42
Given the non-stationarity of inflation series, we opt for an examination of the degree of inflation
persistence using the complementary unit root and stationarity tests. Specifically, we use the
augmented Dickey-Fuller test (Dickey and Fuller, 1981), Phillips-Perron test (Phillips and Perron,
1988) and KPSS test (Kwiatkowski et al., 1992). Given that our data come from a former
transition country, we test the robustness of the results by carrying out a unit root test with a
structural break (Saikkonen and Lütkepohl, 2002, and Lanne et al., 2002, labeled as the LLS test
hereinafter).
For the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, the probability
of rejecting the null hypothesis of a unit root will be reported. The probability can vary from 0 to
1. Higher values thus correspond to more persistence. For example, a probability higher than
0.10 means that the null of a unit root cannot be rejected at the 10% significance level. For the
KPSS stationarity test, the t-statistic will be reported: higher t-statistic values increase the
probability of rejecting the null hypothesis of stationarity and hence characterize more
persistence in the underlying series.
The number of lags in the aforementioned tests for each product is determined according to the
Akaike information criterion. We address the sensitivity of the results by estimating persistence
first for the full sample and then for the restricted sample, i.e. using data only after the
introduction of inflation targeting in 1998.
42 A straightforward application of the non-parametric method to our data does not bring any meaningful insight: the degree of persistence across all sectors is found to be very similar.
Assessing Inflation Persistence - 87 -
Next, we also run a unit root test with a structural break. Given a relatively short time series, we
test for only one structural break on an unknown date (Lanne et al., 2002). As we find that most
of the time series exhibit a structural break around 1998–1999 (shortly after the adoption of
inflation targeting), we decided to employ a unit root test where we impose the break (captured
by the shift dummy) in 1998:1.43 The rationale for imposing the break is to ensure that we subject
each time series to the identical testing procedure and consequently to allow cross-sectional
comparability of our results. We take the t-value from this test as the measure of the persistence
of the series, with a more negative value indicating less persistence (increasing the probability of
rejecting the null hypothesis of a unit root process).
Furthermore, one can put forward a critique that p-values might not generally serve as a universal
measure for the degree of inflation persistence.44 Therefore, we also measure persistence by
simply running the aforementioned stationarity and unit root tests and examining whether we can
reject the corresponding null hypothesis at a reasonable level of significance.45 We then use the
following coding to assess the degree of persistence: 1 if the series is found to contain a unit root,
and 0 if the series is stationary. Subsequently, we calculate the share of unit root processes for
particular sectors. As a result, this exercise provides an additional sensitivity check of our results.
Obviously, the drawback of this measure is that it is not possible to evaluate the extent of
aggregation bias.
It is also vital to note that we use year-on-year inflation rates, for the following reasons. Other
possibilities, such as using month-on-month and quarter-on-quarter changes in the price level, are
associated with seasonality, which may contaminate the true extent of persistence. In addition, 43 Therefore, we estimate the LLS test only for our full sample (1995–2005) and do not estimate the test for the restricted sample (1998–2005, i.e. the inflation targeting period), as we do for the ADF, PP and KPSS tests. 44 Given that p-values are affected by the standard errors of the estimated coefficients, the distribution of p-values is also influenced by the sample size. Hence, p-values cannot be used to compare persistence in, for example, very short versus large samples. Since in our case the sample size is the same for all products (about 100 observations), p-values can be informative in characterizing the non-stationarity properties of the underlying series. 45 More specifically, we use the 5% and 10% significance levels.
Assessing Inflation Persistence - 88 -
these two aforementioned changes are typically not monitored by economic agents such as
households or unions. Most importantly, central banks set their inflation targets in year-on-year
changes in the price level. In addition, Aron and Muellbauer (2006) claim that year-on-year
inflation rates also capture the dynamics of month-on-month inflation.46
3.3 Data
The Czech Statistical Office included 1,022 narrowly defined products in the consumer basket
between 1994 and 2005 on a monthly frequency. Nevertheless, prices of many products were not
tracked over the whole sample period. Typically, the whole consumer basket includes about 700
products on any given date. As a result, we were able to identify 412 individual products for
which the price indexes are available for the whole period spanning from 1994:M1 to 2005:M12.
The selected 412 products represent 64% of the CPI basket for 2005.
As a benchmark, we construct sample inflation as a weighted average of 412 individual price
indices (year-on-year percentage changes). Figure 1 shows the official CPI inflation and our
sample inflation over 1995–2005 at monthly frequency. The high similarity between the two
series suggests that our sample of 412 products is fairly representative in terms of inflation
dynamics. On average, annual CPI inflation in the Czech Republic was about 4.3% over the
period 1994–2005. Prior to 1998, inflation fluctuated around 10%, while successful disinflation
policy resulted in average inflation of around 3% during 1999–2005.
46 Nevertheless, for the purposes of sensitivity checking, we replicate our analysis on month-on-month inflation rates (the results are available upon request). We find that in such case inflation exhibits less persistence compared to the yearly base. A similar observation was pointed out by Altissimo, Ehrmann and Smets (2006): the same series is found to be less persistent if considered in quarter-on-quarter changes compared to year-on-year changes.
Assessing Inflation Persistence - 89 -
Figure 5. Official CPI inflation and sample inflation, 1995–2005
-2
0
2
4
6
8
10
12
14
1619
95/0
1
1996
/01
1997
/01
1998
/01
1999
/01
2000
/01
2001
/01
2002
/01
2003
/01
2004
/01
2005
/01
Sample Official
To facilitate interpretation, the individual 412 products are further grouped into several broader
categories according to their characteristics (in line with the Czech National Bank internal
classification of products for reporting sectoral inflation rates). These are: tradables, non-
tradables, durables, regulated goods and services, non-regulated services, raw goods and
processed goods. Products are also classified by the statistical office into 12 main categories
according to the classification of individual consumption by purpose (COICOP). These
categories are food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and
footwear; housing, water, gas, and electricity; furnishings and maintenance of the house; health
care expenses; transport; communications; leisure and culture; education; hotels, cafés, and
restaurants; and miscellaneous goods and services.
Assessing Inflation Persistence - 90 -
3.4 Results
In the first part, we perform product-specific estimates of inflation persistence using the unit root
(ADF, PP, LLS) and stationarity (KPSS) tests. Then we examine the effect of aggregation on
inflation persistence and analyze whether inflation persistence changes over time. The second
part is devoted to an assessment of the determinants of inflation persistence. Finally, we evaluate
the predictive ability of persistence-weighted core inflation.
4.1 Inflation persistence estimates
The overall distribution of inflation persistence across product categories is summarized in Figure
2 below. The degree of persistence is depicted on the horizontal axis, while the vertical axis
displays the kernel density. Several stylized facts follow from Figure 2.
All three tests suggest that aggregate inflation exhibits significantly higher persistence than the
average inflation persistence as measured at the disaggregate level for the whole sample as well as
for the 1998–2005 sub-period47 (the results of Altissimo, Mojon and Zaffaroni, 2007, and Clark,
2006, for example, also indicate this discrepancy). Generally, there are two possible explanations
for this phenomenon. First, Granger (1980) showed that cross-sectional aggregation of (even
simple) time series may result in complex, often more persistent processes (i.e. aggregation bias).
Typically, the aggregation bias is likely to be greater when there is large heterogeneity in the
product-level inflation persistence. As a result, the estimated persistence of aggregate inflation
may change due to changes in sectoral heterogeneity. Second, it may also reflect the fact that
idiosyncratic shocks vanish due to aggregation. Next, we assess the robustness of these findings
47 The results are valid regardless of whether the sample aggregate inflation is constructed using the mean, weighted mean or median. The gap between aggregate inflation and the average inflation across the disaggregated components is different from zero at the 1% significance level, as suggested by the t-test. However, this significance may be overestimated since the conventional t-test is applied to the test statistics, not to the raw data.
Assessing Inflation Persistence - 91 -
by also running an LLS unit root test with a structural break (Saikkonen and Lütkepohl, 2002,
and Lanne et al., 2002). The break is captured by the shift dummy in 1998:M1. The results from
this test confirm the presence of aggregation bias (see Figure A.2 in the Appendix).
One can also observe a noticeable reduction in overall CPI inflation persistence for the sub-
period 1998–2005 (i.e. the inflation targeting period), while the sample aggregate inflation
persistence has decreased rather marginally (see the lower part of Figure 2). We find that it was
the persistence of tradables (especially durable goods) inflation rather than that of non-tradables
that declined after the adoption of inflation targeting.
Similar evidence of aggregation bias is observed when comparing inflation persistence for the
aggregate CPI and nine sectors (see Table 1 and Table 2; note that the results are obtained by
aggregating the product-specific estimates). Overall, the results in Table 1 and 2 seem to indicate
that inflation persistence in the Czech Republic is higher compared to the euro area members.
While for the Western European countries there are relatively few cases of I(1) processes at
sectoral and even aggregate levels (European Central Bank, 2005), and while the results of
stationarity and unit root tests are often inconclusive48 (Gadea and Mayoral, 2006), the results for
the Czech Republic are much more clear-cut. Czech inflation follows a unit root process for most
of the sectors. On the other hand, Franta et al. (2007) find that aggregate inflation persistence in
the new EU member states tends to be lower than in the euro area when allowing for the time-
varying inflation target.
48 In other words, Gadea and Mayoral find that many sectoral inflation series are fractionally integrated, i.e. follow a process between I(0) and I(1).
Assessing Inflation Persistence - 92 -
Figure 2. Distribution of inflation persistence across 412 products and aggregation bias
ADF 1995–2005 PP 1995–2005 KPSS 1995–2005
ADF 1998–2005 PP 1998–2005 KPSS 1998–2005
Notes: Vertical bold lines denote the persistence of aggregate CPI inflation; simple vertical lines represent the mean of disaggregate inflation persistence. The horizontal axis characterizes the level of inflation persistence (higher values mean more persistence). For all the measures of persistence displayed, higher values mean more persistent inflation. For the ADF and PP unit root tests, the probability of rejecting the null hypothesis of a unit root is reported. The probability can vary from 0 to 1. Higher values correspond to more persistence. For example, a probability higher than 0.10 means that the null of a unit root cannot be rejected at the 10% significance level. For the KPSS stationarity test, the t-statistic is reported. Higher t-statistic values increase the probability of rejecting the null hypothesis of stationarity and hence characterize more persistence in the underlying series.
Moreover, in the Czech case the results of the unit root and stationarity tests are quite similar at
the sectoral level (the test performance at the product level is assessed in the next paragraph). For
example, considering the period from 1995 to 2005 (Table 1), the results of the unit root and
stationarity tests give the same picture: 8 out of the 9 sectors exhibit a unit root process at the
10% significance level; raw goods (line 8) are the only sector which is stationary at the 10% level,
as supported by both the unit root (ADF/PP) and stationarity (KPSS) tests. This similarity
between unit root tests and stationarity tests gives support for I(1) behavior of sectoral inflation
rates. Note that these results are obtained assuming no trend in inflation. The incorporation of a
Assessing Inflation Persistence - 93 -
time trend in the inflation dynamics or accounting for a time-varying inflation target could be
further investigated.
In terms of ranking the persistence across sectors, we find that raw goods consistently exhibit the
smallest inflation persistence. On the other hand, durables inflation seems to be the most inertial.
Interestingly, services and regulated products do not display greater persistence. This finding is
also robust to our alternative indicator of inflation persistence – the share of unit roots. The
attendant results are available in Table A.1 in the Appendix.
Table 1. Inflation persistence, yearly inflation, 1995–2005 (132 obs.)
Measures of persistence Sector No. of products
Sample weights ADF PP KPSS LLS
Tradables 311 0.59 0.31 (0.29) 0.31 (0.27) 0.69** (0.39) -2.35 (1.12) Non-tradables 101 0.41 0.24 (0.21) 0.22 (0.20) 0.55** (0.30) -2.32 (1.03) Services 96 0.40 0.24 (0.21) 0.22 (0.20) 0.56** (0.30) -2.30 (1.05) Non-reg.serv. 74 0.30 0.24 (0.21) 0.21 (0.19) 0.56** (0.30) -2.32 (1.00) Regulated 27 0.11 0.23 (0.21) 0.24 (0.20) 0.53** (0.28) -2.32 (1.13) Durables 164 0.21 0.44 (0.29) 0.43 (0.28) 0.90*** (0.34) -1.86 (0.92) Non-durables 152 0.39 0.16 (0.20) 0.18 (0.18) 0.46* (0.31) -2.88** (1.05) Raw goods 42 0.11 0.07 (0.13) 0.09 (0.11) 0.24 (0.19) -3.43** (1.13) Processed goods 370 0.89 0.32 (0.28) 0.31 (0.26) 0.71** (0.36) -2.22 (1.02) Total prod. level 412 1.00 0.29 (0.28) 0.29 (0.26) 0.66** (0.38) -2.35 (1.09) Aggr. inflation 1 1 0.48 0.49 1.03*** -1.80
Notes: The pairs (tradables, non-tradables) and (raw goods, processed goods) make up a total of 412 products. Durables do not include regulated prices, while processed goods do. For all the measures of persistence displayed, higher values mean more persistent inflation. For the ADF and PP unit root tests, the probability of rejecting the null hypothesis of a unit root is reported. The probability can vary from 0 to 1. Higher values correspond to more persistence. For example, a probability higher than 0.10 means that the null of a unit root cannot be rejected at the 10% significance level. Standard deviations are shown in parentheses. For the KPSS stationarity test, the t-statistic is reported. Higher t-statistic values increase the probability of rejecting the null hypothesis of stationarity and hence characterize more persistence in the underlying series. *, **, and *** denote the 10%, 5% and 1% asymptotical significance levels for rejection of the stationarity hypothesis. Standard deviations are shown in parentheses. For the LLS (Lanne et al., 2002) unit root test in the presence of a structural break, the t-statistic is reported. More negative t-statistic values increase the probability of rejecting the null hypothesis of a unit root and thus characterize less persistence in the underlying series. *, **, and *** denote the 10%, 5% and 1% asymptotical significance levels for rejection of the unit root hypothesis.
Assessing Inflation Persistence - 94 -
Table 2. Inflation persistence, yearly inflation, 1998–2005 (96 obs.)
Measures of persistence Sector No. of products
Sample weights ADF PP KPSS
Tradables 311 0.59 0.21 (0.21) 0.23 (0.19) 0.52** (0.35) Non-tradables 101 0.41 0.23 (0.19) 0.22 (0.17) 0.46* (0.28) Services 96 0.40 0.24 (0.19) 0.22 (0.17) 0.47** (0.29) Non-reg. serv. 74 0.30 0.27 (0.19) 0.25 (0.16) 0.46** (0.27) Regulated 27 0.11 0.12 (0.17) 0.14 (0.16) 0.47* (0.31) Durables 164 0.21 0.24 (0.24) 0.26 (0.23) 0.70** (0.32) Non-durables 152 0.39 0.16 (0.15) 0.20 (0.14) 0.31 (0.25) Raw goods 42 0.11 0.12 (0.14) 0.15 (0.13) 0.16 (0.12) Processed goods 370 0.89 0.22 (0.21) 0.24 (0.19) 0.54** (0.33) Total prod. level 412 1.00 0.21 (0.20) 0.23 (0.19) 0.50** (0.33) Aggr. inflation 1 1 0.26 0.27 0.63**
Notes: As for Table 1.
In addition, our results suggest that inflation persistence has decreased in the post-1998 period,
i.e. since inflation targeting was adopted. Vega and Winkelried (2005) find that inflation targeting
helps in reducing the volatility of inflation; however, the effect on inflation persistence is rather
ambiguous. On the other hand, the results of Levin et al. (2004) indicate that inflation targeters
indeed exhibit smaller inflation persistence. Likewise, Yigit (2007) documents that the adoption
of an inflation target provides a coordinating effect on the inflation expectations of economic
agents and therefore puts downward pressure on inflation persistence.
In this regard, while we find that there are 314 categories out of 412 for which we cannot reject
the null of a unit root based on the ADF test in the 1995–2005 sample at the 5% significance
level, there are 256 such categories in 1998–2005 (note that for the PP test the figures are 339
and 322 categories, respectively). In the case of the KPSS test, we reject the null of stationarity at
the 5% significance level for 269 categories over 1995–2005 and 207 categories for 1998–2005.
These results suggest that inflation persistence may be somewhat lower after the adoption of
inflation targeting in 1998; however, this should be taken with caution, as the power of the tests
Assessing Inflation Persistence - 95 -
may decrease for the shorter sample. Table A.1 presents the detailed results on the (both simple
and consumption-weighted) share of unit root processes, including the LLS test.
We also find that the estimated inflation persistence falls when we control for structural breaks.
This is evident from comparing the ADF and LLS results. The construction of the LLS test
implies that it is essentially the ADF test “adjusted” for the structural break. The results
presented in Table A.1 indicate that the share of unit root processes is indeed smaller for the LLS
test as compared to the ADF test. The results thus comply with Levin and Piger (2004).
At the individual product level, the link between the various tests is illustrated in Figure A1 in the
Appendix. The correlation of the LLS test with the ADF, PP and KPSS tests stands at 0.76, 0.75
and 0.5, respectively. The P-values of the ADF and PP tests are closely related: the corresponding
correlation coefficient is 0.94 for 1995–2005 and 0.87 for 1998–2005. The correlation between
the unit-root tests and the KPSS test for stationarity is fairly high (0.63 and 0.67, respectively) for
1995–2005, and much lower (0.31 and 0.31, respectively) for 1998–2005.
Such a difference over the two periods is likely to be due to the following reasons. First, as the
number of observations decreases the tests lose their power to reject the null hypothesis – that of
an I(1) process for the ADF/PP tests, and of an I(0) process in the case of the KPPS. Second, as
inflation itself has decreased over time, it becomes more difficult to distinguish whether the series
follow an I(0) or I(1) process; the series may become fractionally integrated, as is the case for
disaggregate inflation in West European countries (see Gadea and Mayoral, 2006). In other
words, the growing differences between the unit root and stationarity tests may capture the effect
of structural changes in the Czech Republic and give further indirect support for our supposition
that inflation persistence decreased after the adoption of inflation targeting.
Assessing Inflation Persistence - 96 -
4.2 Explaining inflation persistence
Once the disaggregate estimates of inflation persistence are obtained, we test them for any
significant determinants. In particular, we analyze the ability of product characteristics to explain
the cross-sectional variation in persistence across 412 individual products. In addition, we analyze
the so-called “service inflation persistence puzzle”: several studies have revealed that (labor-
intensive) services, which are typically not subject to international competition, surprisingly
display smaller persistence than goods (see, for example, Altissimo, Mojon and Zaffaroni, 2007;
Clark, 2006; and Coricelli and Horvath, 2006). Thus, our results will add a piece of evidence on
this “service inflation persistence puzzle”. More generally, we analyze the implications of the
degree of competition for inflation persistence.
One hypothesis to explain the variation in inflation persistence is that it differs across sectors.
Concerning the sectoral categories, raw goods indeed demonstrate the lowest inflation
persistence (and the lowest dispersion) among the nine sectors considered. Non-durables have
the second-lowest persistence and dispersion of inflation. Apart from aggregate inflation, the
sector with the highest inflation persistence (and also dispersion) is durables, followed by
processed goods and tradables.
It is interesting to note that services are typically non-tradable and more labor-intensive, i.e. their
prices are likely to be set in a less competitive environment than that for goods. Naturally, the
incentives for price revision for services should then be weaker and thus the convergence to
frictionless equilibrium slower. Consequently, one would expect services prices to display greater
inertia. However, our results – like the empirical evidence – do not support this reasoning. We
find that inflation in services exhibits lower persistence, although for the post-1998 period this
difference diminishes and becomes sensitive to the choice of test. Similarly, Clark (2006) for the
U.S. as well as Coricelli and Horvath (2006) for Slovakia report smaller inflation persistence in
Assessing Inflation Persistence - 97 -
services than for manufacturing using micro level data. Lunnemann and Matha (2004) find that in
about 5 out of 15 EU countries the persistence in services inflation is smaller than the persistence
of the overall HICP.
In this regard, Coricelli and Horvath (2006) put forward an explanation for the finding that
services inflation is often found to exhibit smaller persistence than goods. Typically, it is assumed
that higher competition increases the incentives for price revisions and the market has a tendency
to adjust faster. On the other hand, Calvo (2000) shows that a greater degree of competition may
increase the inertia rather than decrease it. This is because when markets are highly competitive, it
is more likely that individual prices will not diverge far from the average (firms “follow the
pack”)49, otherwise the firm would be pushed out of the market. In other words, the degree of
strategic complementarity among price setters increases with higher competition and individual
pricing decisions will be more affected by the average pricing strategy in the market.
Consequently, greater competition reduces price dispersion; however, it does not have to
decrease persistence.
Price dispersion can be interpreted as a measure of market competition.50 Consequently, this
allows us to test the aforementioned supposition that the degree of competition may indeed be
positively related to inflation persistence. We measure price dispersion as the standard deviation
of price indexes within an individual COICOP category normalized to one in the initial period.
The resulting COICOP-specific measure of price dispersion is obtained by averaging the
standard deviations over time.
49 Note also that deviation from the price of competitors has been found to be one of the most important obstacles to price adjustment in surveys of euro area firms (see Fabiani et al., 2006). 50 A number of recent empirical studies find a negative relationship between price dispersion and the degree of market competition [Baye et al. (2004), Caglayan et al. (2007), Gerardi and Shapiro (2007), Leiter and Warin (2007)].
Assessing Inflation Persistence - 98 -
First, simple pair-wise correlations are illustrated in Table 3. Particularly strong correlations are
detected for the categories of durables and raw goods. We also find a significantly negative
correlation between our measure of price dispersion and inflation persistence. This is robust to
the measure of inflation persistence as well as the sample period.
Table 3 – Correlation matrix – Inflation persistence and product characteristics
1995–2005 1998–2005 ADF PP KPSS LLS ADF PP KPSS Price dispersion -0.25 -0.28 -0.32 -0.18 -0.08 -0.09 -0.27 Durables 0.44 0.45 0.53 0.36 0.13 0.12 0.47 Goods 0.10 0.14 0.14 -0.01 -0.08 0.01 0.05 Non-durables -0.37 -0.33 -0.42 -0.37 -0.20 -0.11 -0.43 Non-tradables -0.11 -0.16 -0.16 0 0.06 -0.02 -0.07 Processed goods 0.28 0.27 0.37 0.34 0.08 0.07 0.34 Raw goods -0.28 -0.27 -0.37 -0.34 -0.08 -0.07 -0.34 Regulated products -0.05 -0.06 -0.09 0 -0.12 -0.14 -0.08 Services -0.11 -0.13 -0.11 0.02 0.05 -0.01 -0.05 Services – non-regulated -0.10 -0.12 -0.08 0.02 0.13 0.09 -0.02 Tradables 0.1 0.16 0.16 0 -0.06 0.02 0.07
Note: Correlation coefficients greater than 0.08 in absolute terms are significant at the 5% level.
Next, we present our results on the determinants of inflation persistence using here the KPSS
test-based estimates of persistence in Table 4. The results suggest that greater price dispersion, a
measure of competition, is associated with smaller inflation persistence, implying that
competition is not conducive to reducing persistence. This finding holds for both our estimation
periods (the full sample, 1995–2005, and the inflation targeting-restricted sample, 1998–2005),
when controlling for product characteristics and altering our estimation technique (OLS vs.
GMM), and, on top of that, is largely unaffected by the measure of persistence (see Tables A.2,
A.3 and A.4 for the results based on targeting-restricted ADF, PP and LLS test-based estimates
of persistence). In addition, we present a logit estimation of the inflation persistence
determinants, which further confirms our findings. Our dependent variable is coded one if the
Assessing Inflation Persistence - 99 -
product inflation is found to follow an I(1) process at the 10% significance level51, and zero
otherwise. The results are available in Table A.5 in the Appendix.
We report both the OLS and GMM estimates to check the robustness of the results. While OLS
may be subject to endogeneity bias, it is known that GMM may give biased results for a smaller
sample. Next, we also control for product characteristics (two products with high correlation
with inflation persistence) and present the results for two sample periods. The Appendix also
contains Table A.6, where we study the impact of product characteristics on inflation persistence.
We find that raw goods as well as non-durables exhibit smaller inflation persistence. There is
some evidence that inflation in the services sector exhibits smaller persistence.
Table 4 – Determinants of inflation persistence
1995–2005 1998–2005 KPSS KPSS KPSS KPSS KPSS KPSS Price dispersion -1.25*** -10.4*** -2.57*** -0.91*** -9.23*** -1.71*** (0.18) (3.85) (0.18) (0.17) (3.53) (0.53) Non-durables -0.17** -0.17*** (0.08) (0.06) Raw goods -0.31*** -0.24*** (0.10) (0.07) Adj. R-squared 0.11 --- --- 0.07 --- --- Estimation method OLS GMM GMM OLS GMM GMM Sargan test (p-value) --- 0.2 (0.15) 0.4 (0.40) --- 1.5 (0.23) 0.9 (0.33) Observations 412 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. The list of instruments for price dispersion is as follows: non-regulated services, non-durables, raw goods and regulated prices dummies. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. P-value in brackets for the Sargan (overidentifying restrictions) test.
To further support our results that competition is likely to be negatively related to inflation
persistence, we present the determinants of price dispersion. Here we expect that non-
tradables/services, as they are typically not subject to international competition, will exhibit
51 The 5% significance level was used as the cut-off point for coding the dependent variable as well. The results remained largely unaffected.
Assessing Inflation Persistence - 100 -
greater price dispersion. Controlling for other product characteristics, the results in Table 5
indicate that the degree of non-tradability of a product, as captured by the services dummy, is
positively linked to price dispersion (see also Crucini et al., 2005).
Table 5 – Determinants of price dispersion
Price dispersion Services – non-regulated 0.06*** 0.09*** 0.07*** 0.09*** 0.11*** (0.01) (0.01) (0.01) (0.01) (0.01) Non-durables 0.07*** 0.07*** 0.08*** (0.01) (0.01) (0.01) Raw goods 0.03*** -0.001*** -0.001 (0.01) (0.001) (0.001) Regulated 0.17*** (0.02) Adj. R-squared 0.06 0.15 0.06 0.15 0.15 Estimation method OLS OLS OLS OLS OLS Observations 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.
4.3 Predictive ability of persistence-weighted core inflation
In order to improve inflation forecasts, a number of core inflation measures have been developed
to capture underlying inflation trends. Generally, the measures remove or reweight the most
volatile categories of inflation, such as energy prices. Smith (2004) notes that core inflation
measures typically exploit cross-sectional information, while time-series information has been
much less noted. In line with this, we construct a measure of core inflation, coretI , based on
product-level inflation rate persistence, giving a greater weight to categories exhibiting greater
persistence, and examine its predictive ability by comparison with other measures of core
inflation as well as various inflation forecasts.
The underlying idea is that the more persistent components of headline inflation may do a good
job in capturing inflation trends. In this context, Cutler (2001) finds that in the case of U.K. data,
Assessing Inflation Persistence - 101 -
persistence-weighted core inflation outperforms other core inflation measures. Cutler (2001)
argues that the exclusion of certain products from the basket in the construction of core inflation
can be arbitrary, and what is more, she finds that certain non-seasonal food prices (food prices
are typically excluded from core inflation) exhibit relatively persistent inflation and thus their
behavior may provide additional information for capturing trends in inflation series.
Our persistence-weighted core inflation, PWcoret
,π , is based on Cutler (2001) and is constructed as
follows:
iti
iPWcore
t p ,
413
1
, ∆=∑=
θπ
where iθ denotes the i-th product inflation persistence (normalized such that 1413
1=∑
=iiθ ) and
itp ,∆ is the i-th product yearly inflation rate at time t. As an alternative indicator, we combine
information on the persistence of an individual product, iθ , and the weight of that product in
the CPI basket in the following way,
iti
iPEWcore
t p ,
413
1
, ∆=∑=
ξπ
where iξ is the simple average of iθ – the individual inflation persistence – and iw is the sample
weight of the i-th product in the CPI basket, where iθ and iw are normalized such that
1413
1=∑
=iiθ and 1
413
1=∑
=iiw . Consequently, we label PEWcore
t,π as the persistence expenditure-
weighted core inflation.
We undertake a simple exercise here to evaluate the predictive ability of persistence-weighted
core inflation vis-à-vis other (core) inflation measures. Specifically, we compare it with net
inflation, median net inflation (the median net individual inflation rate), and so-called adjusted
Assessing Inflation Persistence - 102 -
inflation (net inflation excluding food, beverages and tobacco) over the horizons of 6, 12 and 18
months. The mean square error (MSE) will be used to measure the forecast quality:
( )2
1
,/1 ∑=
+ Π−Π=T
t
iCOREt
CPIhtTMSE ,
where T is the number of observations, h is the time horizon in months and iCOREt
,Π is the
selected core inflation measure.
Figure 3 depicts the predictive ability of the aforementioned core inflation measures. Here we
used the persistence measure based on the ADF test on the 1995–2005 data.52 The results
indicate that adjusted inflation exhibits the smallest MSE and thus is the best predictor of the
core inflation measures considered. Net inflation, median net inflation and persistence-weighted
core inflation, PWcoret
,π , do not perform particularly well. Current inflation and persistence-
weighted core inflation, PWcoret
,π , are relatively good predictors of inflation 6 months ahead, but
their predictive ability worsens substantially over longer time periods.
Figure 3. Predictive ability of core inflation measures, 1995–2005
02468
1012141618
persistenceweighted
pers. exp.weighted
net median net adjusted current
6 months 12 months 18 months
Note: The mean square error is plotted on the vertical axis. 52 The results based on other persistence measures (the PP, KPPS and LLS test-based measures for the full and restricted samples) are similar and available upon request.
Assessing Inflation Persistence - 103 -
3.5 Conclusions
In this paper, we have presented evidence on disaggregate inflation persistence in the Czech
Republic, exploring data from 412 individual narrowly defined products and 9 broader sectors
from 1995:M1 to 2005:M12. The results suggest that inflation persistence decreased after the
adoption of inflation targeting. A somewhat similar observation of falling rather than rising
inflation persistence in the euro area countries over the past decade is reported by the
Eurosystem Inflation Persistence Network (IPN).53 However, inflation persistence in the Czech
Republic still remains relatively high compared to that in the euro area countries.
The results unambiguously point to the presence of aggregation bias, that is, aggregate inflation is
more persistent than the mean of its underlying disaggregated components. This result is robust
to the choice of disaggregation level (412 components or 9 sectors) and weighting scheme
(simple mean, median, or weighted mean), to the choice of estimation technique (unit root ADF,
PP, LLS, or stationarity KPSS tests), and to the choice of period (full sample versus post-1998
inflation targeting period).
We identify that the sectoral structure explains the estimated variation in inflation persistence to a
certain extent. In particular, products belonging to the raw goods category exhibit smaller than
sample average persistence, while durables have higher than average persistence. Concerning the
“services inflation persistence puzzle”, there is evidence that (labor-intensive) services are
characterized by smaller persistence than goods for our 1995–2005 sample. However, the results
are sensitive to the choice of estimation technique and period, i.e. using a shorter sample over
53 A summary of the IPN’s findings is provided by Altissimo, Ehrmann and Smets (2006).
Assessing Inflation Persistence - 104 -
1998–2005 we do not find robust differences in terms of the persistence of goods and services.
Nevertheless, the regression results show that the services dummy is negatively associated with
inflation persistence.
We find that competition is not conducive to reducing inflation persistence. Price dispersion, as a
proxy for the degree of competition, is negatively related to inflation persistence. This finding
confirms the results of Calvo (2000), who shows that as the level of competition increases, the
firm’s pricing strategy is influenced more by the average pricing strategy in the market. The costs
of charging a different price for identical products increase with higher competition. As a result,
there can be a more inertial response to shocks in a more competitive environment.
Lastly, we construct a persistence-weighted core inflation measure and evaluate its predictive
ability by comparison with other available measures of core inflation over the period 1995–2005.
Generally, we find that adjusted inflation (headline inflation excluding regulated prices, fuel and
food prices and changes in indirect taxes) is the best predictor of future inflation trends in our set
of core inflation measures over the horizons of 6, 12 and 18 months. Our proposed measure –
persistence expenditure-weighted core inflation – may be viewed as an equally good predictor as
adjusted inflation for the 6-month horizon, but its predictive ability worsens over longer time
periods.
Assessing Inflation Persistence - 105 -
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APPENDIX Figure A.1 – Link between ADF, PP, KPSS and LLS tests (based on 412 product groups)
ADF vs PP, 1995-2005
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.0
ADF vs PP, 1998-2005
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
LLS vs. ADF, 1995-2005
-8
-7
-6
-5
-4
-3
-2
-1
0
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.
ADF vs KPSS, 1995-2005
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0.00 0.20 0.40 0.60 0.80 1.0
ADF vs KPSS, 1998-2005
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0.00 0.20 0.40 0.60 0.80 1.0
LLS vs. PP, 1995-2005
-8
-7
-6
-5
-4
-3
-2
-1
0
0.00 0.20 0.40 0.60 0.80 1.0
PP vs KPSS, 1995-2005
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0.00 0.20 0.40 0.60 0.80 1.0
PP vs KPSS, 1998-2005
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0.00 0.20 0.40 0.60 0.80 1.0
LLS vs. KPSS, 1995-2005
-8
-7
-6
-5
-4
-3
-2
-1
0
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.
1995-2005corr(adf,pp) 0.94corr(adf,kpss) 0.63corr(pp,kpss) 0.67
1998-2005corr(adf,pp) 0.87corr(adf,kpss) 0.31corr(pp,kpss) 0.31
1995-2005corr(lls,adf) 0.76corr(lls,pp) 0.76corr(lls,kpss) 0.50
Notes: For the ADF and PP tests, the probability of rejecting the null hypothesis of a unit root is employed. The probability can vary from 0 to 1. Higher values correspond to more persistence. For example, a probability higher than 0.10 means that the null of a unit root cannot be rejected at the 10% significance level. For the KPSS stationarity test, the t-statistic is used (shown on the vertical axes). Higher t-statistic values increase the probability of rejecting the null hypothesis of stationarity and hence characterize more persistence in the underlying series. LLS test stands for the Lanne et al. (2002) unit root test with a structural break; the t-statistic is used in the Figure.
Assessing Inflation Persistence - 110 -
Figure A.2 – Distribution of inflation persistence across 412 products and aggregation
bias; Results from Lanne et al. (2002) unit root test with structural break
Notes: The vertical bold line denotes the persistence of aggregate CPI inflation; the simple vertical line represents the mean of the disaggregate inflation persistence. The horizontal axis characterizes the level of inflation persistence (more negative values mean more persistence). Thus, the results are indicative of aggregation bias.
Assessing Inflation Persistence - 111 -
Table A.1 – Inflation persistence, Share of unit root processes
1995–2005 10% significance level Share of unit roots Share of unit roots (weighted)
no_prod sample_w ADF PP KPPS LLS ADF_w PP_w KPPS_w LLS_w Tradables 311 0.59 0.66 0.72 0.73 0.63 0.62 0.71 0.67 0.63 Non_tradables 101 0.41 0.68 0.67 0.76 0.65 0.83 0.79 0.90 0.76 Services 96 0.40 0.70 0.66 0.77 0.67 0.83 0.79 0.90 0.76 Non_regul_serv 74 0.30 0.70 0.66 0.77 0.68 0.81 0.74 0.90 0.72 Regulated 27 0.11 0.63 0.70 0.74 0.59 0.89 0.94 0.91 0.85 Durables 164 0.21 0.85 0.86 0.90 0.82 0.89 0.89 0.80 0.86 Non_durables 152 0.39 0.45 0.57 0.55 0.42 0.47 0.61 0.60 0.51 Raw_goods 42 0.11 0.21 0.29 0.26 0.21 0.29 0.31 0.13 0.33 Processed 370 0.89 0.72 0.75 0.79 0.68 0.75 0.79 0.84 0.73 Total_prod_level 412 1.00 0.67 0.71 0.74 0.64 0.70 0.74 0.76 0.68
5% significance level Share of unit roots Share of unit roots (weighted) no_prod sample_w ADF PP KPPS LLS ADF_w PP_w KPPS_w LLS_w Tradables 311 0.59 0.76 0.82 0.67 0.73 0.76 0.84 0.62 0.75 Non_tradables 101 0.41 0.76 0.80 0.60 0.76 0.86 0.88 0.54 0.82 Services 96 0.40 0.77 0.79 0.60 0.77 0.87 0.88 0.54 0.82 Non_regul_serv 74 0.30 0.74 0.78 0.61 0.76 0.82 0.85 0.59 0.76 Regulated 27 0.11 0.81 0.85 0.59 0.78 0.97 0.98 0.40 0.97 Durables 164 0.21 0.90 0.92 0.87 0.86 0.92 0.94 0.78 0.89 Non_durables 152 0.39 0.59 0.72 0.45 0.59 0.67 0.78 0.53 0.67 Raw_goods 42 0.11 0.36 0.50 0.14 0.38 0.43 0.62 0.09 0.44 Processed 370 0.89 0.80 0.85 0.71 0.78 0.85 0.88 0.65 0.82 Total_prod_level 412 1.00 0.76 0.82 0.65 0.74 0.80 0.86 0.59 0.78
1998–2005 10% significance level Share of unit roots Share of unit roots (weighted)
no_prod sample_w ADF PP KPPS ADF_w PP_w KPPS_w Tradables 311 0.59 0.59 0.70 0.60 0.62 0.73 0.55 Non_tradables 101 0.41 0.69 0.77 0.53 0.82 0.84 0.72 Services 96 0.40 0.73 0.78 0.55 0.83 0.84 0.73 Non_regul_serv 74 0.30 0.82 0.88 0.54 0.94 0.95 0.68 Regulated 27 0.11 0.33 0.48 0.52 0.52 0.56 0.84 Durables 164 0.21 0.63 0.66 0.81 0.62 0.64 0.73 Non_durables 152 0.39 0.54 0.73 0.36 0.61 0.79 0.45 Raw_goods 42 0.11 0.38 0.57 0.07 0.45 0.70 0.02 Processed 370 0.89 0.65 0.73 0.64 0.73 0.79 0.69 Total_prod_level 412 1.00 0.62 0.72 0.58 0.70 0.78 0.62
5% significance level Share of unit roots Share of unit roots (weighted) no_prod sample_w ADF PP KPPS ADF_w PP_w KPPS_w Tradables 311 0.59 0.70 0.77 0.52 0.73 0.82 0.47 Non_tradables 101 0.41 0.77 0.79 0.44 0.84 0.85 0.70 Services 96 0.40 0.79 0.80 0.46 0.84 0.85 0.70 Non_regul_serv 74 0.30 0.91 0.91 0.46 0.96 0.96 0.66 Regulated 27 0.11 0.41 0.48 0.37 0.53 0.56 0.80 Durables 164 0.21 0.71 0.74 0.75 0.70 0.74 0.69 Non_durables 152 0.39 0.68 0.80 0.26 0.74 0.86 0.35 Raw_goods 42 0.11 0.52 0.67 0.05 0.55 0.75 0.01 Processed 370 0.89 0.74 0.79 0.55 0.80 0.84 0.63 Total_prod_level 412 1.00 0.72 0.78 0.50 0.77 0.83 0.56
Assessing Inflation Persistence - 112 -
Table A.2 – Determinants of inflation persistence, ADF test
1995–2005 1998–2005 ADF ADF ADF ADF ADF ADF Price dispersion -0.73*** -6.66*** -1.63*** -0.17* -2.08** -0.58*** (0.14) (2.49) (0.46) (0.1) (1.04) (0.16) Non-durables -0.10** -0.05* (0.05) (0.02) Raw goods -0.16*** -0.002 (0.04) (0.04) Adj. R-squared 0.07 --- --- 0.01 --- --- Estimation method OLS GMM GMM OLS GMM GMM Sargan test (p-value) --- 1.8 (0.19) 1.5 (0.22) --- 0.1 (0.7) 5.2 (0.02) Observations 412 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. P-value in brackets for the Sargan (overidentifying restrictions) test. The list of instruments for price dispersion is as follows: non-regulated services, non-durables, raw goods and regulated prices dummies.
Table A.3 – Determinants of inflation persistence, PP test
1995–2005 1998–2005 PP PP PP PP PP PP Price dispersion -0.73*** -5.66*** -1.51*** -0.17* -0.87*** -0.49*** (0.14) (2.13) (0.42) (0.1) (0.30) (0.18) Non-durables -0.08* -0.01 (0.04) (0.02) Raw goods -0.16*** -0.04 (0.04) (0.03) Adj. R-squared 0.08 --- --- 0.01 --- --- Estimation method OLS GMM GMM OLS GMM GMM Sargan test (p-value) --- 1.9 (0.17) 5.5 (0.02) --- 0.4 (0.82) 5.5 (0.02) Observations 412 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. P-value in brackets for the Sargan (overidentifying restrictions) test. The list of instruments for price dispersion is as follows: non-regulated services, non-durables, raw goods and regulated prices dummies.
Assessing Inflation Persistence - 113 -
Table A.4 – Determinants of inflation persistence, LLS test
1995–2005 LLS LLS LLS Price dispersion -1.99*** -27.7*** -2.69** (0.49) (11.1) (1.24) Non-durables -0.57*** (0.15) Raw goods -0.84*** (0.21) Adj. R-squared 0.03 --- --- Estimation method OLS GMM GMM Sargan test (p-value) --- 2.5 (0.11) 0.6 (0.46)Observations 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. P-value in brackets for the Sargan (overidentifying restrictions) test. The LLS test is a unit root test with a structural break on an unknown date. The test was carried out only for the full sample, 1995–2005; see the main text for explanations. The list of instruments for price dispersion is as follows: non-regulated services, non-durables, raw goods and regulated prices dummies.
Table A.5 – Determinants of inflation persistence, Logit estimates
1995–2005 1998–2005 ADF PP KPSS LLS ADF PP KPSS Price dispersion -3.68*** -2.90** -2.59** -3.45*** -1.37 -0.97 -3.11*** (1.16) (1.15) (1.17) (1.12) (1.11) (1.16) (1.06) Non-durables -1.13*** -0.49* -1.06*** -1.07*** -0.26 0.41 -1.04*** (0.24) (0.25) (0.26) (0.24) (0.23) (0.26) (0.24) Raw goods -1.64*** -1.74*** -1.82*** -1.48*** -0.91*** -0.95*** -2.61*** (0.41) (0.37) (0.42) (0.40) (0.57) (0.37) (0.63) Pseudo R-squared 0.15 0.10 0.15 0.13 0.03 0.06 0.16 Estimation method Logit Logit Logit Logit Logit Logit Logit Observations 412 412 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. The LLS test is a unit root test with a structural break on an unknown date. The test was carried out only for the full sample, 1995–2005; see the main text for explanations.
Assessing Inflation Persistence - 114 -
Table A.6 – Determinants of inflation persistence, Product characteristics
1995–2005 1998–2005 ADF PP KPSS LLS ADF PP KPSS Non-durables -0.24*** -0.20*** -0.34*** -0.79*** -0.08*** -0.04** -0.32*** (0.03) (0.03) (0.03) (0.11) (0.02) (0.02) (0.03) Raw goods -0.16*** -0.16*** -0.34*** -0.86*** -0.01 -0.02 -0.24*** (0.02) (0.02) (0.05) (0.16) (0.03) (0.03) (0.04) Services – nonregulated -0.21*** -0.20*** -0.30*** -0.45*** 0.02 0.01 -0.21*** (0.03) (0.03) (0.04) (0.14) (0.03) (0.03) (0.04) Regulated -0.17*** 0.17*** -0.33*** -0.38*** -0.011*** -0.11*** -0.25*** (0.04) (0.03) (0.06) (0.13) (0.03) (0.03) (0.05) Adj. R-squared 0.24 0.22 0.33 0.21 0.06 0.04 0.29 Estimation method OLS OLS OLS OLS OLS OLS OLS Observations 412 412 412 412 412 412 412
Note: Heteroscedasticity robust standard errors are shown in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively.
Table A.7 – Detailed product-specific results
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsBread, white 1 kg 0.07 0.36 0.41 0.19 -2.08 0.24 0.20 113.43 Bread, whole meal 1 kg 0.30 0.30 0.17 0.40 -2.36 0.17 0.26 94.57 Baguettes (white) 1 kg 0.01 0.33 0.31 0.11 -2.31 0.16 0.13 14.48 Pastry, cake 1 kg 0.19 0.08 0.49 0.09 -2.49 0.28 0.22 19.72 Puff pastry 1 kg 0.40 0.13 0.62 0.07 -1.70 0.39 0.12 5.78 Sponge cake 1 kg 0.47 0.13 0.99 0.38 -1.62 0.41 0.12 6.96 Biscuit dry 1 kg 0.16 0.41 0.49 0.65 -2.38 0.33 0.35 20.20 Biscuit with filling 1 kg 0.00 0.00 0.78 0.33 -3.42 0.00 0.00 20.20 Waffles 1 kg 0.03 0.01 0.79 0.70 -3.42 0.02 0.00 20.20 Wheat flour (impalpable powder)
1 kg 0.11 0.01 0.08 0.19 -2.35 0.15 0.34 7.91
Wheat flour 1 kg 0.13 0.38 0.09 0.22 -2.51 0.24 0.46 13.79 Spaghetti, without eggs 1 kg 0.11 0.46 0.19 0.56 -1.84 0.25 0.28 4.55 Pasta, with eggs 1 kg 0.28 0.39 0.18 0.55 -2.02 0.33 0.35 11.93 Bread dumpling, powder 1 kg 0.01 0.01 0.26 0.09 -3.82 0.10 0.13 8.47 Pudding (powder) 10 pcs 0.46 0.00 0.86 0.23 -2.02 0.34 0.00 5.24 Rice, long-grain 1 kg 0.06 0.32 0.16 0.45 -2.88 0.10 0.26 13.25 Center loin roast 1 kg 0.08 0.27 0.17 0.08 -2.47 0.08 0.32 38.69 Boneless sirloin roast 1 kg 0.04 0.26 0.18 0.08 -2.86 0.04 0.31 38.39 Sirloin chop 1 kg 0.04 0.23 0.21 0.08 -2.74 0.06 0.31 24.34 Boneless blade roast 1 kg 0.07 0.30 0.27 0.08 -2.70 0.06 0.31 24.97 Belly-pork 1 kg 0.07 0.25 0.09 0.09 -2.71 0.09 0.30 21.86 Boneless rump roast 1 kg 0.00 0.00 0.49 0.13 -6.01 0.00 0.07 36.27 Boneless shoulder pot-roast 1 kg 0.00 0.05 0.58 0.14 -5.17 0.00 0.13 22.12 Fore shank 1 kg 0.00 0.08 0.37 0.13 -4.90 0.00 0.14 10.68 Minced meat 1 kg 0.00 0.27 0.34 0.08 -3.79 0.01 0.33 14.67 Liver, Pork 1 kg 0.00 0.16 0.13 0.09 -3.53 0.01 0.30 14.49 Rabbit 1 kg 0.13 0.42 0.73 0.36 -2.61 0.26 0.26 5.03 Veal leg 1 kg 0.02 0.19 0.82 0.52 -4.62 0.01 0.04 2.52
Assessing Inflation Persistence - 115 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsSmall sausage 1 kg 0.04 0.29 0.14 0.09 -3.04 0.05 0.30 26.16 Sausage 1 kg 0.06 0.06 0.16 0.11 -2.71 0.06 0.25 26.16 Salami, Gothaj 1 kg 0.05 0.13 0.38 0.13 -2.46 0.06 0.27 26.16 Ring of Lyoner sausage 1 kg 0.06 0.17 0.15 0.13 -2.79 0.06 0.28 26.16 Salami (ham) 1 kg 0.01 0.00 0.31 0.13 -3.45 0.02 0.36 26.16 Sausage (pepper) 1 kg 0.01 0.02 0.54 0.08 -3.90 0.05 0.27 24.37 Salami, Polican 1 kg 0.09 0.01 0.24 0.14 -2.62 0.10 0.41 24.37 Ham (pork) 1 kg 0.01 0.21 0.19 0.11 -3.06 0.01 0.31 15.44 Sliced bacon 1 kg 0.01 0.11 0.68 0.17 -3.37 0.03 0.31 14.95 Liver pâté 1 kg 0.00 0.34 0.53 0.10 -4.28 0.02 0.25 9.62 Sausage (pork) 1 kg 0.09 0.25 0.59 0.08 -3.44 0.08 0.21 9.16 Sausage (poultry) 1 kg 0.08 0.06 0.56 0.11 -3.01 0.06 0.28 17.87 Luncheon meat 1 kg 0.21 0.32 0.52 0.13 -2.59 0.19 0.32 21.28 Beef (canned meat) 1 kg 0.00 0.18 0.52 0.10 -4.87 0.00 0.37 15.69 Chicken 1 kg 0.01 0.07 0.06 0.06 -3.16 0.09 0.25 67.93 Duck, without heart, liver and gizzard
1 kg 0.03 0.06 0.38 0.17 -3.34 0.11 0.13 7.47
Carp chilled, frozen 1 kg 0.72 0.27 0.77 0.73 -1.16 0.58 0.26 22.72 Salted herring 125 g 0.06 0.28 0.17 0.57 -3.06 0.09 0.28 19.53 Fresh chicken eggs 10 pcs 0.05 0.00 0.08 0.07 -3.34 0.06 0.16 47.16 Milk pasteurized (fat content 1.5%)
1 l 0.18 0.11 0.61 0.12 -2.00 0.28 0.26 22.25
Milk, long life (fat content 1.5%)
1 l 0.04 0.05 0.16 0.06 -3.12 0.07 0.14 66.77
Milk condensed, not sweetened 500 g 0.69 0.30 0.67 0.23 -1.53 0.45 0.20 5.46 Powdered milk, for babies 400 g 0.05 0.04 0.81 0.54 -2.41 0.19 0.15 9.08 Mellow cheese (Ermine) 1 kg 0.59 0.05 1.06 0.55 -1.63 0.55 0.06 12.21 Processed cheese (not flavored) 1 kg 0.71 0.18 0.97 0.25 -2.29 0.63 0.24 39.56 Cottage cheese (LUCINA) 1 kg 0.28 0.08 0.90 0.48 -1.57 0.32 0.18 5.33 Fermented milk products, liquid 1 l 0.56 0.12 0.86 0.14 -2.42 0.46 0.11 10.58 Cream, sweet 1 l 0.43 0.22 0.79 0.20 -1.93 0.38 0.20 28.88 Natural yoghurt, fat content low 150 g 0.62 0.31 0.66 0.18 -1.87 0.54 0.24 44.65 Fruit yoghurt 150 g 0.24 0.08 0.94 0.18 -2.12 0.23 0.08 66.97 Ice-cream 1 l 0.26 0.12 0.25 0.11 -2.19 0.17 0.40 24.86 Butter, unsalted 1 kg 0.10 0.29 0.17 0.32 -2.21 0.18 0.37 39.91 Pure lard 1 kg 0.00 0.27 0.08 0.09 -3.45 0.00 0.29 6.44 Olive oil 1 l 0.16 0.57 0.41 0.33 -2.36 0.30 0.58 2.19 Sunflower oil 1 l 0.07 0.07 0.69 0.53 -2.38 0.07 0.35 16.28 Margarine, type Hera 1 kg 0.08 0.12 0.59 0.29 -1.96 0.13 0.17 14.67 Margarine, type Planta 1 kg 0.27 0.59 0.94 0.80 -2.53 0.23 0.54 5.70 Fresh apples 1 kg 0.01 0.01 0.19 0.08 -3.10 0.05 0.10 32.28 Fresh peaches, nectarines 1 kg 0.00 0.00 0.17 0.25 -3.51 0.00 0.00 18.70 Fresh grapes 1 kg 0.00 0.00 0.15 0.08 -3.71 0.01 0.00 21.22 Fresh water melon 1 kg 0.02 0.06 0.10 0.16 -3.33 0.00 0.00 11.09 Fresh oranges 1 kg 0.00 0.00 0.22 0.13 -3.98 0.02 0.04 41.74 Fresh lemons 1 kg 0.00 0.02 0.20 0.15 -3.73 0.01 0.01 11.08 Fresh bananas 1 kg 0.00 0.04 0.20 0.24 -3.54 0.00 0.00 41.40 Fresh kiwis 1 kg 0.00 0.00 0.08 0.14 -3.16 0.02 0.08 6.21 Dried raisins 1 kg 0.09 0.07 0.11 0.29 -2.63 0.21 0.23 6.72 Potatoes 1 kg 0.00 0.02 0.22 0.11 -6.69 0.01 0.01 30.29 Frozen chipped potatoes 1 kg 0.00 0.00 0.09 0.28 -4.15 0.15 0.03 12.75
Assessing Inflation Persistence - 116 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsPotato dumpling (powder) 1 kg 0.00 0.36 0.77 0.39 -2.98 0.46 0.32 3.00 Fresh white cabbage 1 kg 0.00 0.00 0.08 0.08 -3.75 0.05 0.08 8.28 Fresh cucumber 1 kg 0.00 0.00 0.17 0.11 -4.74 0.00 0.00 15.18 Fresh green peppers 1 kg 0.00 0.00 0.14 0.14 -3.93 0.00 0.00 22.46 Fresh tomatoes 1 kg 0.00 0.00 0.05 0.06 -5.35 0.00 0.00 23.74 Fresh cauliflower 1 kg 0.00 0.00 0.16 0.17 -4.34 0.00 0.00 10.04 Fresh carrots 1 kg 0.00 0.00 0.09 0.08 -4.53 0.01 0.02 5.42 Fresh celeriac 1 kg 0.00 0.00 0.12 0.17 -3.99 0.03 0.12 7.62 Fresh cultivated mushrooms 1 kg 0.33 0.10 0.41 0.14 -1.99 0.30 0.08 3.90 Garlic (dry) 1 kg 0.10 0.12 0.49 0.15 -2.29 0.10 0.28 10.32 Cabbage, jar 1 kg 0.03 0.15 0.20 0.47 -2.98 0.02 0.23 6.07 Pickled gherkins 1 kg 0.00 0.06 0.36 0.09 -4.65 0.00 0.26 4.97 Dried lentils 1 kg 0.10 0.27 0.14 0.19 -2.71 0.16 0.35 4.20 Jam, strawberry 1 kg 0.00 0.37 0.48 0.77 -6.16 0.07 0.41 0.40 Granulated sugar 1 kg 0.03 0.04 0.08 0.11 -3.04 0.17 0.15 31.13 Confectioner’s sugar 1 kg 0.11 0.20 0.13 0.15 -3.09 0.19 0.27 8.68 Chocolate, milk 100 g 0.02 0.13 0.97 0.57 -2.18 0.03 0.17 34.97 Chocolate dessert 250 g 0.24 0.13 1.04 0.41 -2.27 0.24 0.17 32.68 Chocolate bar 100 g 0.15 0.04 0.13 0.10 -2.52 0.00 0.02 13.23 Fruit drops 100 g 0.29 0.05 1.16 0.73 -1.86 0.35 0.07 10.18 Chewing gum 1 pack 0.05 0.53 0.30 0.26 -2.78 0.04 0.30 10.39 Cake from egg yolk 10 pcs 0.54 0.32 0.91 0.17 -2.55 0.55 0.28 16.81 Sherbet 1 l 0.35 0.24 0.20 0.68 -1.81 0.29 0.24 10.92 Honey 1 kg 0.29 0.00 0.33 0.12 -2.01 0.39 0.00 0.97 Meat extract 100 g 0.05 0.22 0.43 0.67 -3.07 0.06 0.34 15.35 Delicious salad 1 kg 0.00 0.07 0.64 0.23 -3.70 0.00 0.11 9.31 Table salt 1 kg 0.59 0.14 0.70 0.29 -1.70 0.49 0.16 13.29 Black pepper 100 g 0.03 0.03 0.47 0.36 -3.02 0.17 0.23 9.39 Tomato ketchup 1 kg 0.20 0.13 0.14 0.11 -3.13 0.11 0.13 11.48 Mustard 1 kg 0.37 0.37 0.22 0.32 -1.89 0.43 0.52 13.05 Yeast 1 kg 0.27 0.00 0.35 0.30 -1.94 0.34 0.00 10.78 Roust coffee beans 100 g 0.00 0.57 0.29 0.18 -6.60 0.00 0.38 20.56 Instant coffee 100 g 0.00 0.00 0.70 0.55 -5.40 0.00 0.22 22.11 Black tea bags 100 g 0.08 0.00 0.49 0.71 -2.77 0.24 0.01 19.39 Green tea bags 100 g 0.17 0.00 0.37 0.54 -1.93 0.25 0.00 9.44 Coffee substitutes 100 g 0.21 0.01 0.58 0.36 -1.74 0.44 0.00 8.85 Fruity syrup 1 kg 0.07 0.15 0.19 0.85 -1.98 0.39 0.15 24.41 Orange juice 1 l 0.14 0.06 0.22 0.18 -2.08 0.15 0.06 27.11 Spring water 1 l 0.05 0.19 0.28 0.15 -2.80 0.01 0.07 43.21 Mineral water (fizzy) 1 l 0.10 0.03 0.87 0.21 -2.66 0.09 0.00 59.40 Coca-cola (Pepsi-cola) 1 l 0.00 0.00 0.08 0.15 -4.04 0.03 0.00 11.80 Rum (domestic production) 1 l 0.20 0.29 0.07 0.24 -3.00 0.06 0.10 39.33 Vodka (fine) 1 l 0.05 0.12 0.41 0.45 -3.50 0.07 0.19 31.05 Fernet stock (liqueur) 1 l 0.54 0.54 0.14 0.24 -1.64 0.20 0.30 70.74 Becher’s (Carlsbad) liqueur 1 l 0.15 0.13 0.28 0.28 -2.32 0.25 0.19 42.80 Scotch whisky 1 l 0.61 0.32 0.67 0.85 -1.25 0.44 0.32 13.89 White wine (high quality) 1 l 0.61 0.00 0.98 0.41 -1.99 0.61 0.00 74.11 Red wine (high quality) 1 l 0.78 0.06 0.91 0.43 -1.51 0.68 0.23 69.88 Sparkling wine semi-dry 0,75 l 0.03 0.01 0.53 0.06 -3.09 0.16 0.07 38.17 Italian vermouth 1 l 0.30 0.09 0.96 0.38 -2.31 0.32 0.11 11.76 Bottled light beer 0,5 l 0.27 0.14 0.86 0.67 -1.72 0.36 0.19 232.41
Assessing Inflation Persistence - 117 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsBottled light lager 0,5 l 0.08 0.15 0.80 0.82 -3.07 0.23 0.29 57.37
PETRA (filter tipped cigarettes) 1
package 0.10 0.35 0.82 0.56 -2.50 0.14 0.44 221.14 SPARTA LIGHT (filter tipped cigarettes)
1 package 0.11 0.27 0.71 0.56 -2.62 0.17 0.40 147.43
START (filter tipped cigarettes) 1
package 0.10 0.22 1.01 0.77 -2.48 0.16 0.40 73.71 MARLBORO (filter tipped cigarettes)
1 package 0.01 0.09 0.76 0.50 -3.45 0.03 0.25 81.47
Clothing materials for business suit (wool)
1 m2 0.46 0.48 0.74 0.56 -2.09 0.43 0.49 5.11
Briefs (for men) 1 pc 0.66 0.09 0.81 0.96 -1.20 0.40 0.09 13.02 Men’s pajamas (cotton) 1 pc 0.95 0.59 1.20 1.03 -0.62 0.92 0.57 5.87 Men’s shirt (classic) 1 pc 0.92 0.19 1.06 0.71 -0.62 0.87 0.20 20.53 Men’s waistcoat 1 pc 0.96 0.92 1.31 1.02 -0.33 0.95 0.88 10.65 Men’s sweatshirt 1 pc 0.82 0.12 1.22 1.02 -0.75 0.88 0.21 17.20 Panty made of cotton (ladies underwear)
1 pc 0.68 0.21 0.96 1.07 -0.68 0.68 0.14 13.22
Bra 1 pc 0.89 0.01 1.13 0.80 -0.93 0.80 0.01 19.68 Nightdress 1 pc 0.81 0.39 1.28 1.08 -0.74 0.83 0.39 7.38 Swimsuit 1 pc 0.57 0.53 1.28 1.00 -1.63 0.55 0.51 8.21 Ladies pullover – long-sleeved 1 pc 0.94 0.67 1.32 1.02 -0.87 0.93 0.63 16.23 Ladies tracksuit 1 pc 0.91 0.32 1.29 1.11 -0.15 0.91 0.30 5.83 Panty made of cotton (girl’s underwear)
1 pc 0.62 0.68 0.70 0.97 -0.97 0.55 0.69 5.44
Children’s pajamas (cotton) 1 pc 0.83 0.25 1.04 1.01 -0.77 0.73 0.24 6.02 Tracksuit 1 pc 0.90 0.61 1.29 0.99 -0.56 0.88 0.58 14.49 Children’s sweatshirt (cotton) 1 pc 0.91 0.74 1.32 1.18 -1.07 0.91 0.75 13.69 Men’s suit 1 pc 0.81 0.04 1.22 1.01 -0.84 0.80 0.04 7.87 Men’s jacket (for summer) 1 pc 0.79 0.58 1.28 1.23 -1.11 0.80 0.59 6.93 Men’s jacket (for winter) 1 pc 0.65 0.03 1.01 0.32 -1.52 0.65 0.03 18.67 Men’s trousers 1 pc 0.80 0.01 1.20 1.10 -1.33 0.78 0.00 21.25 Men’s jacket (leather) 1 pc 0.50 0.46 1.14 0.92 -2.28 0.51 0.45 5.42 Ladies overcoat 1 pc 0.05 0.26 1.23 0.88 -1.70 0.05 0.25 8.75 Ladies winter coat 1 pc 0.50 0.13 1.10 0.55 -1.41 0.52 0.13 17.42 Ladies windcheater (for winter) 1 pc 0.71 0.12 1.17 0.63 -1.35 0.76 0.14 18.41 Two-piece suit 1 pc 0.65 0.01 1.21 0.80 -1.14 0.43 0.04 22.86 Ladies jacket (for summer) 1 pc 0.11 0.01 1.27 0.92 -1.77 0.08 0.01 13.40 Ladies trousers (wool) 1 pc 0.93 0.36 1.17 0.77 -0.52 0.93 0.36 17.34 Dress (for summer) 1 pc 0.32 0.03 1.10 0.61 -3.05 0.31 0.03 21.72 Smock 1 pc 0.44 0.08 1.32 1.16 -1.21 0.43 0.06 31.14 Skirt 1 pc 0.14 0.02 1.35 1.13 -1.27 0.14 0.01 13.21 Dress (for girls) 1 pc 0.78 0.89 1.32 1.22 -0.53 0.83 0.88 5.73 Children’s trousers (cotton) 1 pc 0.66 0.84 1.31 1.21 -1.89 0.67 0.86 18.22 Men’s socks (cotton) 1 pair 0.73 0.09 1.05 0.48 -1.16 0.77 0.13 12.90 Ladies socks (cotton) 1 pair 0.54 0.08 0.78 0.15 -1.57 0.49 0.07 6.61 Ladies tights 1 pc 0.44 0.42 0.88 0.79 -1.55 0.52 0.41 14.86 Children’s tights 1 pc 0.76 0.03 0.94 0.78 -0.99 0.77 0.02 5.38 Ladies neckerchief 1 pc 0.00 0.45 0.99 1.01 -4.23 0.00 0.45 4.53 Handkerchief 1 pc 0.47 0.34 0.65 0.29 -1.61 0.46 0.34 1.29 Men’s leather gloves 1 pair 0.58 0.08 0.60 0.30 -1.76 0.54 0.03 5.25 Thread, sewing 500 m 0.02 0.05 0.36 0.64 -3.30 0.00 0.50 1.24 Knitting yarn 100 g 0.20 0.13 0.46 0.73 -2.10 0.18 0.35 2.84
Assessing Inflation Persistence - 118 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsZip fastener 1 pc 0.26 0.11 1.05 0.62 -3.10 0.26 0.24 2.61 Cleaning of overcoat 1 pc 0.35 0.14 0.67 0.08 -2.44 0.25 0.11 8.80 Shortening or elongation of coat 1 repair 0.05 0.12 1.00 0.32 -3.17 0.04 0.10 4.34 Men’s footwear suitable for everyday (leather)
1 pair 0.49 0.15 1.31 1.00 -1.57 0.50 0.16 21.31
Men’s footwear suitable for summer (leather)
1 pair 0.77 0.09 1.30 1.11 -1.10 0.83 0.06 8.09
Men’s footwear suitable for winter (leather)
1 pair 0.77 0.28 1.20 0.82 -1.24 0.73 0.27 13.15
Ladies footwear suitable for everyday (leather)
1 pair 0.83 0.04 1.27 1.07 -0.77 0.85 0.06 33.56
Ladies footwear suitable for summer (leather)
1 pair 0.76 0.03 1.29 1.08 -1.23 0.79 0.01 23.05
Ladies footwear for home wear (textile)
1 pair 0.66 0.02 1.12 0.58 -1.72 0.67 0.02 6.47
Children’s footwear suitable for everyday (leather)
1 pair 0.83 0.18 1.30 1.18 -0.61 0.88 0.17 6.25
Children’s footwear suitable for summer (leather)
1 pair 0.16 0.10 1.36 1.20 -2.20 0.41 0.12 6.07
Children’s footwear for leisure wear (leather)
1 pair 0.75 0.17 1.20 0.73 -1.31 0.72 0.17 7.17
Children’s footwear for leisure wear (textile)
1 pair 0.34 0.02 0.83 0.14 -2.19 0.37 0.02 7.17
Children’s footwear for home wear (textile)
1 pair 0.22 0.00 0.85 0.26 -3.49 0.18 0.00 4.60
Children’s footwear suitable for winter (plastic)
1 pair 0.50 0.19 0.98 0.28 -1.88 0.36 0.19 5.84
Repair of ladies heel (replace old with new heels promptly)
1 pair 0.56 0.02 0.80 0.92 -1.61 0.52 0.01 100.55
Actual rentals paid by tenants, first category – 3 rooms, rent regulated by the government
monthly
0.52 0.03 0.80 0.91 -1.62 0.51 0.01 118.25 Actual rentals paid by tenants, first category – 4 rooms, rent regulated by the government
monthly
0.53 0.03 0.80 0.91 -1.58 0.52 0.01 48.57 Actual rentals paid by tenants, second category – 2 rooms, rent regulated by the government
monthly
0.46 0.04 0.74 0.87 -1.71 0.45 0.02 20.07 Actual rentals paid by tenants, first category – 2 rooms, cooperative flat
monthly
0.12 0.30 0.40 0.80 -1.11 0.10 0.25 79.91 Actual rentals paid by tenants, first category – 3 rooms, cooperative flat
monthly
0.31 0.51 0.42 0.91 -0.74 0.26 0.44 105.21 Actual rentals paid by tenants, first category – 4 rooms, cooperative flat
monthly
0.18 0.44 0.39 0.89 -1.36 0.15 0.40 26.76 Imputed rentals of owner-occupied flat – 2 rooms
monthly 0.24 0.49 0.61 1.01 -0.91 0.20 0.40 157.25
Imputed rentals of owner-occupied flat – 3 rooms
monthly 0.38 0.62 0.41 0.93 -0.53 0.32 0.60 393.47
Imputed rentals of owner-occupied flat – 4 rooms
monthly 0.40 0.66 0.48 0.66 -1.33 0.29 0.59 717.89
Tiles 1 m2 0.62 0.55 1.05 0.88 -1.43 0.54 0.49 17.69 Washbasin 1 pc 0.44 0.00 0.69 0.79 -1.28 0.43 0.00 13.54 Mixer tap 1 pc 0.38 0.08 0.60 0.54 -1.37 0.46 0.08 15.06 Decorator 1 m2 0.47 0.18 0.55 0.33 -1.60 0.37 0.18 13.15
Assessing Inflation Persistence - 119 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsPainter 1 m2 0.52 0.08 1.03 0.49 -1.64 0.53 0.10 13.61 Tiler 1 m2 0.02 0.08 0.59 0.29 -3.24 0.03 0.07 26.89 Heating engineer 1 hour 0.47 0.01 0.87 0.50 -0.93 0.52 0.00 11.05 Paraffin oil 1 l 0.01 0.01 0.20 0.10 -3.62 0.07 0.10 0.40 Black coal 100 kg 0.02 0.38 0.73 0.66 -3.97 0.02 0.41 8.16 Brown coal 100 kg 0.37 0.43 0.73 0.69 -1.85 0.37 0.45 32.96 Briquettes (made from brown coal)
100 kg 0.31 0.29 1.05 0.63 -1.84 0.35 0.31 3.13
Coke 100 kg 0.02 0.09 0.10 0.15 -2.97 0.08 0.25 3.37 Firewood 100 kg 0.26 0.01 0.86 0.38 -2.68 0.25 0.01 5.54 Heat for fuel and preparation of hot water
1 GJ 0.21 0.20 0.45 0.53 -1.54 0.18 0.20 523.14
Upholstered chair 1 pc 0.53 0.40 1.26 1.01 -2.33 0.51 0.39 14.13 Kitchen dining table 1 pc 0.00 0.00 1.05 0.79 -4.85 0.00 0.00 10.83 Wardrobe 1 pc 0.22 0.03 0.34 0.20 -2.08 0.16 0.03 26.31 Studio couch 1 pc 0.03 0.23 0.88 0.19 -3.21 0.05 0.18 32.78 Kitchen unit 1 set 0.00 0.13 1.01 1.19 -4.87 0.00 0.13 27.91 Wall system 1 set 0.33 0.48 1.14 0.72 -1.65 0.34 0.50 26.11 Table in the garden 1 pc 0.68 0.86 0.57 0.32 -1.82 0.35 0.74 4.69 Table lamp 1 pc 0.10 0.01 1.02 0.44 -2.35 0.11 0.05 31.21 Woven carpet 1 m2 0.21 0.00 1.10 0.84 -1.63 0.27 0.00 32.15 Tufted carpet 1 m2 0.16 0.00 0.87 0.10 -3.85 0.22 0.00 8.75 Upholstered armchair repair 1 repair 0.26 0.00 0.59 0.64 -2.13 0.28 0.00 5.92 Quilt 1 pc 0.08 0.13 0.71 0.76 -3.02 0.17 0.13 8.69 Blanket (synthetic fiber) 1 pc 0.13 0.12 0.64 0.51 -1.55 0.47 0.36 8.69 Decorative textile made of cotton
1 m2 0.46 0.01 1.11 0.51 -1.85 0.61 0.01 14.97
Knitted synthetic curtains 1 m2 0.37 0.26 0.57 0.79 -1.02 0.29 0.26 12.84 Bed linen (not crape) 1 set 0.24 0.00 0.47 0.67 -3.23 0.16 0.00 11.88 Bed linen (crape) 1 set 0.70 0.65 0.76 0.50 -1.36 0.43 0.48 11.88 Bed sheet made of cotton 1 pc 0.11 0.57 0.73 0.63 -2.04 0.27 0.49 6.79 Terry towel 1 pc 0.80 0.19 1.12 0.94 -0.89 0.66 0.44 7.34 Dishcloth 1 pc 0.80 0.11 1.08 0.63 -1.76 0.61 0.11 4.12 Refrigerator 1 pc 0.21 0.00 1.11 0.97 -2.26 0.60 0.00 8.09 Freezer 1 pc 0.21 0.00 0.75 0.73 -1.77 0.40 0.00 9.58 Washing machine 1 pc 0.82 0.02 1.25 1.18 -0.92 0.78 0.02 52.97 Dishwasher 1 pc 0.75 0.07 0.96 1.11 -1.02 0.75 0.09 17.93 Electric range (with a grill) 1 pc 0.97 0.83 1.21 1.12 -0.17 0.95 0.81 7.90 Microwave oven 1 pc 0.41 0.02 0.92 0.72 -1.72 0.47 0.04 20.84 Electric boiler 1 pc 0.59 0.23 0.60 0.73 -1.47 0.51 0.23 8.10 Vacuum cleaner 1 pc 0.85 0.06 1.22 0.88 -0.95 0.80 0.07 26.04 Sewing machine 1 pc 0.73 0.48 0.76 0.78 -1.05 0.69 0.44 3.10 Electric hand-held beater 1 pc 0.74 0.65 1.23 1.12 -1.23 0.83 0.76 8.44 Electric deep fryer 1 pc 0.54 0.57 0.25 0.59 -1.49 0.39 0.57 5.18 Iron 1 pc 0.28 0.03 1.03 1.07 -2.02 0.59 0.04 4.32 Repair of a refrigerator 1 repair 0.19 0.05 1.14 0.79 -2.10 0.07 0.12 14.01 Repair of a washing machine 1 repair 0.80 0.21 1.19 0.90 -0.92 0.74 0.29 16.37 Fireproof bowl 1 pc 0.77 0.67 1.18 0.83 -1.52 0.73 0.62 11.23 Mug (porcelain) 1 pc 0.81 0.01 1.04 0.81 -1.19 0.74 0.02 5.80 Plate (porcelain) 1 pc 0.76 0.60 1.13 0.86 -1.12 0.72 0.57 7.47 Cup and saucer (pottery) 1 pc 0.70 0.00 0.83 0.96 -1.54 0.57 0.00 5.30 Vase 1 pc 0.51 0.22 1.00 0.58 -2.12 0.49 0.21 13.66
Assessing Inflation Persistence - 120 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsFrying pan 1 pc 0.08 0.45 1.20 0.85 -1.96 0.08 0.42 5.11 Cutlery 6 pcs 0.14 0.00 0.29 0.20 -2.88 0.04 0.00 3.30 Kitchen knife 1 pc 0.13 0.30 0.29 0.41 -2.68 0.10 0.30 3.71 Soup ladle 1 pc 0.17 0.12 0.93 0.20 -2.54 0.17 0.10 5.17 Mixing/wooden spoon 1 pc 0.26 0.26 0.75 0.69 -1.95 0.10 0.28 5.17 Kitchen scales 1 pc 0.54 0.39 1.05 0.90 -1.61 0.44 0.63 3.86 Bucket 1 pc 0.48 0.44 0.91 1.07 -1.98 0.22 0.51 5.19 Ironing board 1 pc 0.34 0.42 1.24 0.94 -1.28 0.44 0.46 3.27 Lawn mower (type: rotary mower, electric)
1 pc 0.47 0.00 1.03 0.91 -1.83 0.34 0.00 21.20
Electric drill 1 pc 0.36 0.01 0.71 0.08 -2.10 0.23 0.11 10.59 Screwdriver 1 pc 0.52 0.12 1.16 1.03 -1.35 0.51 0.06 8.33 Lawn rake (with wooden handle) 1 pc 0.14 0.10 0.86 0.48 -1.53 0.01 0.10 5.89 Rocker switch 1 pc 0.52 0.12 1.23 1.16 -1.00 0.71 0.10 3.47 Light bulb 1 pc 0.08 0.01 0.74 0.20 -2.71 0.15 0.03 8.95 AA battery 1.5 V 1 pc 0.01 0.04 0.89 0.66 -3.41 0.01 0.04 8.95 Nails 1 kg 0.03 0.04 0.23 0.40 -3.19 0.13 0.31 8.97 Detergent 1 kg 0.21 0.14 0.73 1.01 -2.28 0.28 0.38 66.95 Anticalcareous for washing machine, powder
1 kg 0.31 0.07 0.13 0.49 -2.30 0.27 0.09 3.65
Dish washing liquid 1 l 0.10 0.17 0.57 0.50 -2.58 0.24 0.35 13.59 Liquid scourer 1 l 0.20 0.30 0.28 0.41 -2.54 0.18 0.35 15.31 Furniture polish 1 l 0.57 0.22 0.94 0.95 -1.28 0.56 0.22 1.55 Broom 1 pc 0.00 0.51 0.91 0.80 -2.71 0.00 0.34 7.82 Insecticide 200 ml 0.06 0.22 1.14 0.86 -3.03 0.06 0.22 1.74 Adhesive 50 ml 0.22 0.26 0.43 0.36 -1.79 0.14 0.49 4.97 Paper napkin 100 pcs 0.03 0.35 0.49 0.22 -3.57 0.26 0.17 7.56 Plastic bag 50 pcs 0.00 0.48 0.31 0.64 -4.93 0.01 0.52 5.22 Aluminum foil 1 m2 0.51 0.14 0.60 0.21 -1.69 0.38 0.10 3.47 Scissors 1 pc 0.33 0.27 1.14 0.68 -1.86 0.50 0.33 1.43 Carpet cleaning 1 m2 0.13 0.02 0.13 0.35 -2.32 0.05 0.23 5.16 Laundry 1 amount 0.55 0.27 0.50 0.16 -1.80 0.35 0.20 4.34 ACYLPYRIN 10 pcs 0.12 0.06 0.56 0.34 -2.37 0.13 0.28 5.24 ATARALGIN 20 pcs 0.01 0.00 0.63 0.31 -2.98 0.13 0.00 5.78 CELASKON 250 30 pcs 0.06 0.08 0.13 0.09 -2.91 0.18 0.30 7.86 B KOMPLEX FORTE 20 pcs 0.01 0.04 0.24 0.36 -2.85 0.10 0.33 7.86 Chamomile 50 g 0.06 0.14 0.40 0.12 -2.65 0.16 0.27 5.93 Medical thermometer 1 pc 0.33 0.02 0.51 0.17 -1.84 0.18 0.01 1.35 Medical examination at the request of a patient
1 service0.44 0.00 0.27 0.74 -1.99 0.20 0.00 4.26
plastic surgery – eyelids 1 service 0.04 0.14 0.37 0.51 -3.04 0.03 0.10 10.00 Partly removable tooth replacement
1 pc 0.06 0.00 0.50 0.23 -2.71 0.12 0.00 13.79
Eye refraction 1 service 0.04 0.17 0.78 0.47 -2.49 0.06 0.17 12.97 Tire casing (bike) 1 pc 0.12 0.40 1.03 0.70 -2.19 0.12 0.43 10.31 Tire (radial) 165 R 13 1 pc 0.65 0.45 1.28 1.00 -1.22 0.80 0.45 22.00 Battery L1 12V 1 pc 0.54 0.67 0.25 0.19 -2.02 0.26 0.45 4.98 Petrol 95 1 l 0.20 0.34 0.07 0.10 -2.46 0.10 0.21 193.24 Petrol Super 98 1 l 0.31 0.46 0.07 0.12 -2.16 0.12 0.26 38.39 Diesel for car 1 l 0.16 0.47 0.07 0.11 -2.41 0.16 0.31 29.01 Engine oil 1 l 0.10 0.12 0.36 0.68 -1.81 0.36 0.50 8.84 Centering of rear wheel (bicycle) 1 repair 0.16 0.47 0.46 0.49 -2.30 0.27 0.35 7.83
Assessing Inflation Persistence - 121 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 Weights
Charge for driving licenses course
fee 0.05 0.00 0.53 0.17 -1.77 0.30 0.10 32.29 Parking charge for cars 1 hour 0.13 0.57 0.50 0.39 -2.25 0.07 0.43 7.16
Motorway tax disc annual
fee 0.30 0.11 0.69 0.41 -2.12 0.36 0.23 15.32 Individual fare in public urban transport by bus
1 ticket 0.38 0.30 0.55 0.34 -2.11 0.34 0.13 1.71
Payments for the delivery of a letter inland
1 pc 0.02 0.02 0.10 0.09 -3.35 0.01 0.02 10.87
Payments for the delivery of a parcel inland
1 pc 0.06 0.03 0.47 0.14 -3.35 0.04 0.03 1.76
Installation costs of private telephone equipment
1 pc 0.00 0.00 0.31 0.30 -6.59 0.00 0.00 2.49
Television set – color 1 pc 0.80 0.35 1.30 1.18 -0.78 0.83 0.38 12.73 Hi-fi music centre 1 pc 0.92 0.38 1.06 0.82 -0.83 0.87 0.36 4.69 Film for color prints (36 pictures)
1 pc 0.25 0.48 1.07 0.89 -1.31 0.25 0.45 12.79
Repair of color TV set 1 repair 0.82 0.80 0.91 1.13 -0.83 0.80 0.75 26.25 Guitar (not electric and not for children)
1 pc 0.49 0.00 1.02 0.34 -1.76 0.52 0.00 10.95
Doll (from PVC) 1 pc 0.43 0.56 1.04 0.51 -1.56 0.42 0.56 5.49 Toy car (with an electric cell) 1 pc 0.89 0.79 1.29 1.14 -0.85 0.84 0.79 6.99 Building set (type Lego) 1 pc 0.77 0.44 1.24 1.08 -1.67 0.75 0.47 10.38 Soft toy 1 pc 0.48 0.10 1.07 0.52 -1.77 0.48 0.10 4.36 Inflatable ball 1 pc 0.50 0.02 1.01 0.53 -1.76 0.51 0.02 2.89 Baby carriage (toy) 1 pc 0.67 0.04 1.06 0.39 -1.54 0.67 0.04 1.64 Ball (for volleyball) 1 pc 0.07 0.38 1.02 0.28 -2.41 0.07 0.25 8.83 Tent 1 pc 0.30 0.88 1.15 0.83 -1.05 0.29 0.81 7.61 Rucksack 1 pc 0.22 0.29 0.72 0.97 -2.29 0.41 0.43 5.18 Carnation 1 pc 0.00 0.10 0.61 0.28 -2.94 0.00 0.11 11.22 Rose 1 pc 0.11 0.00 1.02 0.49 -2.43 0.15 0.01 11.22 Pot plants (type African violet) 1 pc 0.63 0.11 0.94 0.37 -1.56 0.51 0.09 5.76 Artificial flower 1 pc 0.01 0.21 0.67 0.76 -2.66 0.02 0.22 8.97 Outdoor plant – garden bush (rose)
1 pc 0.51 0.23 1.01 0.36 -1.85 0.51 0.20 4.22
Dog-food, dried 500 g 0.19 0.47 0.31 0.22 -2.24 0.21 0.43 38.77 Veterinary service 1service 0.17 0.22 0.12 0.14 -2.68 0.07 0.18 8.11 Ticket, ski lift 1 pc 0.14 0.18 0.13 0.19 -2.95 0.11 0.23 16.36 Ticket, aerobics centre or fitness centre
1 hour 0.43 0.23 0.83 0.68 -1.83 0.41 0.24 9.50
Swimming pool, indoor 1 ticket 0.21 0.21 0.61 0.71 -1.52 0.28 0.24 9.09 Ticket, football game average 0.32 0.41 0.42 0.89 -1.90 0.30 0.41 3.05 Charge for dancing lessons (adolescent people)
course fee 0.02 0.16 1.01 0.89 -4.04 0.02 0.13 3.92
Ticket, cinema average 0.77 0.05 0.76 0.99 -0.96 0.74 0.02 10.41 Ticket, theatre average 0.26 0.00 0.77 0.63 -2.49 0.15 0.00 16.48 Ticket, concert average 0.01 0.10 0.37 0.60 -2.52 0.01 0.24 7.67 Lending fee, video cassette 24 hours 0.24 0.29 0.11 0.24 -2.49 0.15 0.22 5.89 Blow-up of a color picture 10 pcs 0.19 0.35 0.37 0.33 -2.51 0.13 0.30 21.91 Developing color film 36 prints 1 pc 0.14 0.27 0.15 0.09 -2.73 0.07 0.18 5.56 License for radio – monthly monthly 0.09 0.01 0.43 0.33 -2.87 0.06 0.01 30.66 License for television – monthly monthly 0.16 0.03 0.16 0.29 -2.61 0.12 0.03 68.37 Children’s book (aged 9 years or less)
average 0.10 0.02 0.46 0.31 -2.11 0.15 0.02 13.25
Assessing Inflation Persistence - 122 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsBelles letters by domestic author average 0.16 0.00 0.59 0.39 -2.37 0.08 0.00 11.66 Belles letters by worldwide-known author
average 0.15 0.10 0.17 0.17 -2.38 0.15 0.10 23.31
Daily newspaper MLADA FRONTA DNES
monthly 0.13 0.04 0.41 0.17 -2.77 0.14 0.05 12.55
Daily newspaper, tabloid – BLESK
monthly 0.12 0.12 0.33 0.55 -3.96 0.10 0.10 18.48
Daily newspaper – PRAVO monthly 0.03 0.03 0.62 0.45 -2.83 0.03 0.03 13.68 Daily newspaper – LIDOVE NOVINY
monthly 0.48 0.28 0.44 0.11 -1.84 0.30 0.21 12.15
Picture postcard 10 pcs 0.14 0.00 0.32 0.22 -2.70 0.11 0.00 4.72 Desk calendar 1 pc 0.55 0.20 0.81 0.76 -1.71 0.55 0.21 5.96 Domestic recreation – stay in the mountains
1 person 0.22 0.01 0.94 0.34 -2.22 0.21 0.01 63.51
Spain 1 person 0.03 0.16 0.15 0.21 -3.18 0.03 0.11 65.08
Italy 4
persons 0.01 0.17 0.20 0.26 -3.07 0.00 0.00 20.62 School-fees at nursery school monthly 0.48 0.57 1.15 0.99 -1.10 0.56 0.57 12.73 Tuition at private secondary school
monthly 0.46 0.52 0.96 0.77 -1.69 0.46 0.50 7.83
School-fees at higher level than secondary school
yearly 0.06 0.06 0.19 0.75 -3.05 0.06 0.08 2.97
Examination fee for entrance to university
fee 0.77 0.45 1.07 1.19 -0.97 0.77 0.45 1.81
Language teaching 1 hour 0.02 0.24 1.00 0.56 -3.59 0.01 0.22 15.20 School fees at art school (lower level)
yearly 0.17 0.12 1.03 0.54 -2.26 0.17 0.12 13.25
After-school care centre fee monthly 0.50 0.83 0.49 0.29 -1.75 0.29 0.80 2.09 Thick soup 0,33 l 0.02 0.04 0.65 0.21 -3.73 0.17 0.04 4.45 Meat soup 0,33 l 0.22 0.25 0.70 0.11 -2.45 0.13 0.16 5.56 Roast sirloin in cream sauce 100 g 0.02 0.30 0.63 0.18 -4.21 0.02 0.30 27.05 Beef goulash 100 g 0.00 0.13 0.71 0.14 -4.92 0.00 0.13 34.49 Pork roast 100 g 0.03 0.34 0.49 0.28 -4.01 0.02 0.26 92.53 Schnitzel 100 g 0.02 0.45 0.33 0.48 -3.16 0.03 0.45 82.67 Pepper with minced meat filling 100 g 0.00 0.19 0.67 0.18 -4.53 0.02 0.18 24.98 Cheese deep fried in breadcrumbs
100 g 0.20 0.34 0.18 0.24 -2.34 0.15 0.27 10.32
Dumplings (side dish) 160 g 0.07 0.40 0.65 0.17 -2.82 0.08 0.27 36.30 Sliced ham – starter 70 g 0.00 0.37 0.22 0.49 -5.03 0.02 0.27 13.11 Pancake – warm dessert 100 g 0.13 0.35 0.10 0.16 -2.41 0.07 0.20 6.46 Coffee 1 portion 0.00 0.39 0.57 0.13 -4.53 0.02 0.20 18.38 Coke (Pepsi Cola) in a restaurant 0,2 l 0.04 0.25 0.18 0.34 -2.94 0.03 0.18 14.68 Draught beer – light 0,5 l 0.04 0.14 0.55 0.27 -2.78 0.10 0.21 47.21 Draught beer – light (lager) 0,5 l 0.19 0.17 0.13 0.37 -2.62 0.21 0.26 16.79 Light beer (lager) 0,5 l 0.02 0.08 0.13 0.09 -2.64 0.02 0.12 2.31 White wine 0,2 l 0.27 0.29 0.36 0.41 -1.75 0.36 0.29 10.19 Red wine 0,2 l 0.31 0.22 0.47 0.34 -1.63 0.33 0.25 7.45 Inland rum – dark 0,05 l 0.17 0.32 0.44 0.31 -2.01 0.13 0.35 1.57 Spirit, brandy – FERNET STOCK
0,05 l 0.34 0.28 0.59 0.35 -2.13 0.24 0.35 5.25
Spirit, Becher’s (Carlsbad) liqueur
0,05 l 0.28 0.27 0.69 0.41 -2.09 0.37 0.37 2.05
Assessing Inflation Persistence - 123 -
Products Units ADF95 ADF98 KP95 KP98 LLS PP95 PP98 WeightsA two or three-course meal (lunch or supper) in canteens
1 menu 0.10 0.45 0.36 0.18 -3.12 0.08 0.26 236.03
Lunch in canteens in schools – pupils aged 7–10 years
1 menu 0.30 0.44 0.61 0.52 -1.84 0.18 0.32 50.20
Lunch in canteens in schools – pupils aged 11–14 years
1 menu 0.35 0.47 0.66 0.53 -1.89 0.21 0.30 58.71
Lunch in canteens in (secondary) schools – students aged 15 years or more
1 menu
0.10 0.45 0.66 0.51 -2.04 0.18 0.30 33.58 Lunch in canteens in universities 1 menu 0.09 0.18 0.13 0.27 -2.36 0.17 0.33 13.84 Hotel **** 1 night 0.28 0.27 1.09 0.91 -1.00 0.41 0.45 2.22 Hotel *** 1 night 0.43 0.19 1.17 0.85 -1.59 0.15 0.12 8.94 Hostel 1 night 0.38 0.30 0.64 0.55 -1.67 0.35 0.32 3.97 Cottage 1 night 0.18 0.26 0.92 0.66 -1.70 0.28 0.26 7.40 Accommodation services of universities
monthly 0.86 0.79 0.15 0.30 -0.98 0.79 0.68 7.38
Barber 1 service 0.11 0.00 0.46 0.15 -1.91 0.23 0.15 13.19 Hairdresser (for ladies) 1 service 0.31 0.34 0.52 0.15 -1.89 0.15 0.30 71.49 Deep complexion clearing incl. face pack
1 service0.79 0.42 0.99 0.44 -1.30 0.61 0.32 21.11
Hair dryer 1 pc 0.65 0.00 0.98 0.56 -2.13 0.49 0.01 6.16 Electric razor 1 pc 0.35 0.48 1.04 0.92 -2.05 0.26 0.41 7.40 Toilet soap 100 g 0.69 0.49 1.14 0.99 -1.28 0.64 0.47 26.92 Toothpaste 75 ml 0.39 0.44 0.96 0.99 -1.30 0.29 0.42 27.87 Toilet paper 1 pc 0.01 0.25 0.18 0.52 -2.78 0.04 0.31 27.29 Toothbrush 1 pc 0.45 0.00 0.91 0.40 -3.10 0.35 0.00 10.87 Non-electrical razor 1 pc 0.17 0.19 0.51 0.65 -2.68 0.21 0.42 7.96 Hair shampoo 250 ml 0.79 0.43 1.10 0.87 -0.98 0.64 0.18 19.95 Cream NIVEA 150 ml 0.12 0.12 0.61 0.89 -2.33 0.12 0.14 30.73 Deodorant 100 g 0.29 0.50 0.95 0.69 -2.63 0.50 0.30 20.21 Lipstick 1 pc 0.63 0.46 0.81 1.08 -1.22 0.60 0.46 22.51 Ladies wrist watch 1 pc 0.08 0.07 0.58 0.61 -2.81 0.06 0.08 15.21 Wedding ring (gold) 1 pc 0.00 0.03 0.16 0.19 -3.46 0.01 0.03 20.48 Electronic wall clock 1 pc 0.21 0.22 0.97 0.50 -4.38 0.23 0.22 8.08 Ladies umbrella 1 pc 0.75 0.15 0.81 0.75 -1.04 0.55 0.38 5.27 Pram 1 pc 0.79 0.03 1.03 0.61 -1.02 0.64 0.32 1.53 Accommodation in old people’s home
monthly 0.26 0.03 1.01 0.78 -2.17 0.35 0.03 63.31
Cremation fee 0.12 0.22 0.15 0.16 NA 0.09 0.19 1.50 Registration fee for a dog fee 0.12 0.21 0.45 0.35 -2.61 0.09 0.17 3.78
Note: Sample weight multiplied by 100.
Assessing Inflation Persistence - 124 -
APPENDIX 2
In Appendix 2, we re-estimate Table 1 and 2 using different time coverage (e.g. 1995-1997 pre-
targeting period and 1999-2001 targeting period) to assess the supposed fall in inflation
persistence further. Therefore, we keep the sample size identical. Obviously, the drawback is that
the sample size is rather small.
The results presented in Table A.8 and A.9 show that the estimated degree of inflation
persistence falls for almost all categories after the adoption of inflation targeting in 1998, albeit
the standard error of estimates is large and thus, the results should be interpreted with caution.
Table A.8. Inflation persistence, yearly inflation, 1995–1997 (36 obs.)
Measures of persistence Sector No. of products
Sample weights ADF PP KPSS
Tradables 311 0.59 0.42 (0.31) 0.46 (0.30) 0.41* (0.17) Non-tradables 101 0.41 0.45 (0.28) 0.49 (0.26) 0.42* (0.19) Services 96 0.40 0.45 (0.28) 0.48 (0.27) 0.43* (0.19) Non-reg. serv. 74 0.30 0.44 (0.27) 0.54 (0.27) 0.44* (0.17) Regulated 27 0.11 0.48 (0.31) 0.47 (0.26) 0.36* (0.22) Durables 164 0.21 0.46 (0.31) 0.46 (0.30) 0.42* (0.17) Non-durables 152 0.39 0.37 (0.30) 0.45 (0.29) 0.41* (0.17) Raw goods 42 0.11 0.30 (0.32) 0.37 (0.32) 0.42* (0.16) Processed goods 370 0.89 0.44 (0.30) 0.48 (0.29) 0.41* (0.18) Total prod. level 412 1.00 0.43 (0.31) 0.47 (0.29) 0.41* (0.17) Aggr. inflation 1 1 0.49 0.32 0.14
Notes: The pairs (tradables, non-tradables) and (raw goods, processed goods) make up a total of 412 products. Durables do not include regulated prices, while processed goods do. For all the measures of persistence displayed, higher values mean more persistent inflation. For the ADF and PP unit root tests, the probability of rejecting the null hypothesis of a unit root is reported. The probability can vary from 0 to 1. Higher values correspond to more persistence. For example, a probability higher than 0.10 means that the null of a unit root cannot be rejected at the 10% significance level. Standard deviations are shown in parentheses. For the KPSS stationarity test, the t-statistic is reported. Higher t-statistic values increase the probability of rejecting the null hypothesis of stationarity and hence characterize more persistence in the underlying series. *, **, and *** denote the 10%, 5% and 1% asymptotical significance levels for rejection of the stationarity hypothesis. Standard deviations are shown in parentheses. For the LLS (Lanne et al., 2002) unit root test in the presence of a structural break, the t-statistic is reported. More negative t-statistic values increase the probability of rejecting the null hypothesis of a unit root and thus characterize less
Assessing Inflation Persistence - 125 -
persistence in the underlying series. *, **, and *** denote the 10%, 5% and 1% asymptotical significance levels for rejection of the unit root hypothesis.
Table A.9. Inflation persistence, yearly inflation, 1999–2001 (36 obs.)
Measures of persistence Sector No. of products
Sample weights ADF PP KPSS
Tradables 311 0.59 0.36 (0.29) 0.40 (0.30) 0.40* (0.17) Non-tradables 101 0.41 0.22 (0.28) 0.24 (0.28) 0.36* (0.18) Services 96 0.40 0.22 (0.28) 0.24 (0.28) 0.37* (0.18) Non-reg. serv. 74 0.30 0.19 (0.27) 0.20 (0.26) 0.36* (0.16) Regulated 27 0.11 0.32 (0.27) 0.35 (0.29) 0.37* (0.22) Durables 164 0.21 0.32 (0.29) 0.34 (0.30) 0.39* (0.17) Non-durables 152 0.39 0.36 (0.31) 0.42 (0.31) 0.39* (0.17) Raw goods 42 0.11 0.40 (0.26) 0.51 (0.27) 0.38* (0.17) Processed goods 370 0.89 0.32 (0.30) 0.35 (0.30) 0.39* (0.18) Total prod. level 412 1.00 0.33 (0.30) 0.37 (0.30) 0.39* (0.17) Aggr. inflation 1 1 0.001 0.07 0.54**
Notes: See Table A.8.
The Effects of Monetary Policy - 126 -
4 The Effects of Monetary Policy in the Czech Republic: An
Empirical Study *
4.1 Introduction
Understanding the transmission of monetary policy to inflation and other real economic variables
is of key importance if central bankers are to conduct monetary policy effectively. Not
surprisingly, there is extensive theoretical as well as empirical literature studying the effects of
monetary policy shocks on real economy aggregates and prices. For a small open economy such
as the Czech Republic, it is vital to analyze monetary policy transmission for several reasons.
First, there is somewhat mixed evidence regarding monetary policy transmission, as many studies
estimate standard vector autoregression (VAR) models mixing data from two distinct policy
regimes, i.e., from the fixed exchange rate regime under which the Czech National Bank
conducted its policy until May 1997, and from the inflation targeting regime that was adopted in
January 1998.54 Not surprisingly, the identification of monetary policy shocks then becomes
somewhat cumbersome and all these studies exhibit the price puzzle (see our Table 1 in the
results section).
* We thank Oxana Babetskaia-Kukharchuk, Sophocles Brissimis, Martin Cincibuch, Michal Franta, Paolo Giordani, Jan Hanousek, Jaromír Hurník, Marek Jarocinski, Michal Kejak, Evžen Kočenda, Francesco Lippi, Benoit Mojon, Michal Skořepa, Wadim Strielkowski, Petr Zemčík, and seminar participants at the Austrian National Bank, the Czech National Bank, Charles University, and the International Trade and Finance Association Annual Conference for helpful comments and discussions. All remaining errors are entirely our own. The views expressed in this paper are not necessarily those of the Czech National Bank. This paper was supported by Czech National Bank Research Project No. A3/07.
54 See Coats, Laxton, and Rose (2003) and Kotlán and Navrátil (2003) for an overview of Czech monetary policy.
The Effects of Monetary Policy - 127 -
Therefore, it is worthwhile to update previous results reflecting the monetary policy regime
changes, to utilize a wider range of econometric techniques and, on top of that, to incorporate
real-time and forward-looking variables into the VAR analysis. To our knowledge, real-time data
has not been applied to study monetary transmission in the Czech Republic. This is in a sense
paradoxical, as an important feature of monetary policy conduct is that it is based on the
information set available at the time of policy-making. This implies that using ex-post revised
data (note that these are typically more precise, but are not available at the time of monetary
policy action) may contaminate the estimated effects of monetary policy (Croushore and Evans,
2006). The revisions are typical for output data.55
There is also no empirical evidence about monetary policy effects on sectoral prices. This is
striking, because tradable prices in a small open economy may be driven to a large extent by
international factors that domestic monetary policy is unlikely to affect. Our prior assumption is
that as non-tradable prices are typically less exposed to international competition and more labor-
intensive, the reaction of non-tradable prices is likely to be more persistent (see e.g. Barro, 1972,
and Martin, 1993, for models relating the degree of competition to price rigidity).56
In this paper, we examine the effects of monetary policy within the vector autoregression (VAR),
structural VAR (SVAR), and factor-augmented VAR (FAVAR) frameworks during the inflation
targeting period in the Czech Republic. More specifically, we focus on assessing the persistence
and magnitude of monetary policy shocks on output (including the real-time output gap), prices
55 We therefore utilize the real-time estimates of the output gap available from the Czech National Bank (CNB). Using the central bank output gap is advantageous for monetary policy shock identification, as the central bank conducts its policy based on its estimate of the degree of economic activity, not the estimates of other institutions or individuals. Note that price indices are not revised ex post by the Czech Statistical Office. An additional rationale for using the output gap is that in an environment of changing potential growth of the economy, as is the case in our sample, actual GDP growth does not necessarily give an accurate picture about the degree of economic activity. 56 The negative link between the degree of competition and price rigidity is also documented empirically using microeconomic data at the price-setter level by Alvarez and Hernando (2006) for the euro area and Coricelli and Horvath (2006) for Slovakia.
The Effects of Monetary Policy - 128 -
(at both the aggregate and sectoral level) and the exchange rate, controlling for a standard set of
factors.
The paper is organized as follows. Section 2 discusses the related literature. The data are
presented in section 3. Section 4 is focused on identification issues. Section 5 contains our results
on the effects of monetary policy. We present our conclusions in section 6, and an appendix
follows.
4.2 Related VAR Literature
Vector autoregressions (VARs), as introduced by Sims (1980), are considered to be benchmarks
in econometric modeling of monetary policy transmission. It has been argued that this class of
models provides a certain mix between a mere “data-driven” approach and an approach
coherently based on economic theory (see Fry and Pagan, 2005, on the applications of VARs for
macroeconomic research). In terms of monetary policy analysis, the VAR methodology has been
further developed among others by Gerlach and Smets (1995); Leeper, Sims, and Zha (1998); and
Christiano, Eichenbaum, and Evans (1999). The last-mentioned study provides a detailed review
of the literature on this topic in the United States. Similarly, there has been extensive research
undertaken in Europe to study various aspects of monetary transmission in the euro area
countries (see Angeloni, Kashyap, and Mojon, 2003). The research on monetary transmission in
the euro area either focuses on euro area-wide analysis (Peersman and Smets, 2001) or studies
specific countries in detail (Mojon and Peersman, 2001).
The economic theory suggests that output and prices should temporarily fall after a monetary
contraction. Nevertheless, as regards prices, a number of papers document that, on the contrary,
prices rise after a monetary contraction. This effect has been labeled as the “price puzzle.” The
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literature typically argues that the price puzzle is a consequence of some model misspecification
(Brissimis and Magginas, 2006, and Giordani, 2004). Meanwhile, Barth and Ramey (2001) suggest
that a fall in both prices and output would indicate that monetary policy affects the economy
mainly through the demand channel. On the other hand, falling output and rising price levels
would point to the prevalence of the supply or cost channel.57
In addition, the literature examines the effect of monetary policy on exchange rate behavior.
Generally, an immediate exchange rate appreciation after a monetary tightening and then a
gradual depreciation of the domestic currency is expected according to uncovered interest rate
parity. However, the empirical evidence is again somewhat mixed. Some authors find a rather
persistent appreciation of the domestic currency (“delayed overshooting”, Eichenbaum and
Evans, 1995), while others report that the exchange rate actually depreciates with a monetary
contraction and provide explanations for the so-called exchange rate puzzle (Kim and Roubini,
2000).
A number of approaches to dealing with model misspecification related to monetary policy shock
identification have been stressed in the literature. For example, Brissimis and Magginas (2006)
show that by adding forward-looking variables such as federal funds futures to a standard VAR
specification, one is able to obtain responses to monetary policy that are consistent with the
theory. The rationale for including federal funds futures is that they contain market expectations
about future monetary policy action (this expectation element may also be found in commodity
prices or money, to a certain extent).
57 If a firm has to borrow to finance its production, interest rates enter its cost function. Consequently, a monetary policy tightening increases the firm’s costs, to which the firm may react by increasing the price of the products it sells. In consequence, this argument suggests that the price puzzle does not have to be caused by model misspecification. In general, see Coricelli et al. (2006) for more specific explanations of the price puzzle.
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In addition, Croushore and Evans (2006) emphasize the role of data revisions for monetary
policy shock identification. Monetary policy makers react to the information set available at the
time they make their decision, and it is often the case that GDP data are revised afterwards. As a
result, using ex-post GDP data series may contaminate the estimated monetary policy effects.
Also, monetary policy makers often tend to react to the output gap rather than GDP growth. In
addition, Giordani (2004) shows that using the output gap instead of GDP growth alleviates the
price puzzle. These concerns are especially appealing in our case. First, the CNB’s main
forecasting model (the so-called Quarterly Projection Model) indeed contains an output gap in its
reaction function (Coats et al., 2003). Second, GDP growth may still be useful as the measure of
the degree of economic activity if potential output growth is not changing much. However, in the
case of the Czech economy, it is estimated that potential output growth sharply increased from
some 2% in 1998 to around 5.5% in 2005 (Dybczak, Flek, Hájková, and Hurník, 2006).
Next, there has been a lot of research focusing on the sensitivity of the responses of aggregate
variables such as aggregate inflation and output to monetary policy within the VAR framework.
However, much less is known about the responses to monetary policy at the more disaggregated
level. Erceg and Levin (2006) find that the durable goods sector is more sensitive to interest rate
changes than the non-durable goods sector in the U.S. Based on this empirical finding, they
investigate the impact of monetary policy on these two industries and find, as expected, that
monetary policy effects are much stronger in the durable goods industry. Dedola and Lippi
(2005) study the responses to monetary policy of various industrial sectors for a number of
OECD countries. They find that the responses vary between sectors in terms of their magnitude
and persistence. This result is confirmed by Peersman and Smets (2005), who find a number of
significant differences between various industries in the euro area in terms of both the magnitude
of the output response as well as the asymmetry of the responses over the business cycle.
The Effects of Monetary Policy - 131 -
Bouakez et al. (2005) is one of the few studies that examine the impact of monetary policy on
disaggregate prices. Their results suggest that monetary transmission affects household
consumption in the construction and durable manufacturing sectors the most, but the impact of
a monetary policy shock vanishes relatively quickly. They also find significant differences between
the sectors’ inflation in terms of variance decomposition, volatility, and persistence. Bouakez et al.
(2005) find that the response of services inflation to monetary policy shocks is relatively
pronounced and also the most persistent. Boivin et al. (2007) study the effect of macroeconomic
fluctuations on disaggregate prices within the factor-augmented VAR framework. Among other
things, their results indicate that the degree of market power explains the diversity of the
responses of disaggregate prices to monetary policy shocks.
Several papers study the monetary policy effects for the Czech Republic within the VAR
framework.58 Using the sample period after the adoption of inflation targeting (1998–2004),
Hurník and Arnoštová (2005) find that prices respond with a peak around 5–6 quarters after a
shock, although there is some evidence for a price puzzle in the first two quarters after the shock.
Output falls after a monetary contraction, with a peak after one year or so. There is a delayed
overshooting of the exchange rate, as it depreciates only some 4 to 5 quarters after the monetary
policy innovation. Extending the sample back to 1994, when the fixed exchange rate regime was
in use, yields less satisfactory results, as it is obviously more difficult to identify monetary policy
shocks across two monetary policy regimes. In our paper, we use a similar, slightly extended time
horizon (after the adoption of inflation targeting), but we opt for monthly rather than quarterly
data. In addition, in our paper we include the real-time output gap in the benchmark
specification, as opposed to the ex-post revised GDP used by Hurník and Arnoštová (2005). The
effects of a monetary policy contraction estimated in our paper are largely in line with the
responses observed in more developed economies and countries in the Eurozone, in particular. 58 See Coricelli, Égert, and MacDonald (2006) for a survey of current findings on monetary policy transmission in Central and Eastern Europe, including those undertaken within the VAR framework.
The Effects of Monetary Policy - 132 -
Contrary to Hurník and Arnoštová (2005), we do not find evidence for a price puzzle in the
Czech Republic.
Next, there are a number of papers analyzing and comparing the effects of monetary policy in
groups of Central and Eastern European countries vis-à-vis other, more advanced economies
(Creel and Levasseur, 2005; Darvas, 2006; EFN59, 2004; Héricourt, 2005). Many studies find
evidence of price and/or exchange rate puzzles for the Czech Republic. As argued by Coricelli et
al. (2006), the price puzzle is generally avoided in studies that allow for changes in coefficients
and in papers employing more sophisticated identification schemes. As we argue below, the price
puzzle in these studies often arises because monetary policy regime changes are ignored. In our
paper we consider only the period after the change of monetary policy regime, characterized by
stable coefficients (as assessed by the estimation of recursive coefficients).
Among the studies that do not find evidence of a price puzzle, Jarocinski (2006) provides a
Bayesian VAR analysis of monetary policy effects in Western and Central Europe. Interestingly,
Jarocinski finds that monetary policy is more potent in Central Europe, despite a lower level of
financial development and smaller indebtedness. Regarding the Czech Republic, he uncovers that
there is a relatively strong appreciation of exchange rates as well as a larger price decline after a
monetary policy innovation, as compared to other Central European countries. Elbourne and
Haan (2006) study the interactions between the financial system and monetary transmission
within the structural VAR framework for a group of ten Central and Eastern European countries.
For the Czech Republic, they find a hump-shaped response of prices, an exchange rate
appreciation, and a fall in industrial production after a monetary policy innovation. Next,
financial structure is found to be of little importance for monetary transmission.
59 European Forecasting Network (2004)
The Effects of Monetary Policy - 133 -
4.3 Data
This section contains a description of our dataset. We restrict our sample to the data from 1998
onwards, i.e., since the inflation targeting framework was adopted by the Czech National Bank
(previously – until May 1997 – it had operated a fixed exchange rate regime). Our sample thus
spans from 1998:1 to 2006:5 at monthly frequency. While studies in this stream of literature often
employ quarterly data, given the length of our sample we decided to work at monthly frequency.
As a result, we have 101 observations. The source of our data is the CNB’s public database
ARAD (except for the output gap, which is only available internally within the CNB). The plots
of all the series are available in Appendix 1.
We use GDP, lgdpt, and the real-time output gap estimate, outputgaprealt, as measures of economic
activity.60 GDP is traditionally used for this kind of exercise, but Giordani (2004) suggests using
the output gap. In addition, by using the real-time output gap estimate we avoid the risk resulting
from the use of ex-post data, which are not available to central bankers at the time of monetary
policy formulation (Croushore and Evans, 2005). As GDP and the output gap are only available
at quarterly frequency, we interpolate these two using the quadratic-match average procedure.61
Note that all the other variables we use are not revised afterwards.
Next, we employ the net price index, lnett, (the net price index is the consumer price index
excluding regulated prices). For our disaggregate analysis, we employ the tradable price index,
tradablet, and the non-tradable price index, nontradablet. Note that the individual components
underlying the consumer price indices are grouped into tradables and non-tradables categories in
line with the internal CNB classification.
60 See Coats et al. (2003, chapter 5) on the construction of the output gap used by the CNB. The output gap is the difference between actual and potential output, where the latter is estimated by a multivariate filter, more specifically by the Kalman filter procedure, where the system of equations is in the state-space representation. 61 We admit that interpolation introduces information not available at the time of policy making.
The Effects of Monetary Policy - 134 -
Further, the nominal CZK/EUR exchange rate, lexratet, and the three-month interbank interest
rate (3M PRIBOR62), pribort, are used. To capture external developments, the 1-year EURIBOR,
euribort, and the commodity price index, lcommodityt, are utilized. The forward rate agreement rate
(9*12 FRA rate), frat, is used to bring in an additional forward-looking element. Given that there
are no futures or forwards in the Czech Republic that are directly linked to the monetary policy
rate (2W repo) as is the case in the U.S., we decided to use forwards on interbank rates, which are
very closely related to the policy rate. Finally, all data are in logs except interest rates and the real-
time output gap.
4.4 Identification
In this section, we discuss the VAR framework we adopt.63 The choice of variables for our VAR
model is largely motivated by an open economy New Keynesian model (see, for example, Gali
and Monacelli, 2005). The main equations of this class of models are aggregate demand, the
Phillips curve, the monetary policy rule, and uncovered interest rate parity.
We estimate two benchmark models and then undertake a sensitivity analysis. The difference
between these two benchmark models is that the first includes only the aggregate price index,
while the second distinguishes between the tradable and non-tradable price indices. The
specification of the first baseline model is the following:
ttptt uXLBYLAY ++= − )()( (1)
62 The actual monetary policy instrument of the CNB is the 2W repo rate. Since the repo rate is not changed continuously and is censored, we opt for the 3M PRIBOR, which is very closely linked to the 2W repo rate; its correlation stands at 0.998 in our sample. In addition, the 3M PRIBOR may capture central bank communication. See Horvath (2008) for a discussion related to the use of the monetary policy rate vs. the interbank market rate in the Czech Republic. 63 See Rudebusch (1998) and Sims (1998) on the discussion regarding appropriateness of VAR modelling for monetary policy.
The Effects of Monetary Policy - 135 -
where Yt and Xt represent endogenous and exogenous variables64, respectively. The data vectors
are: Yt = outputgaprealt, lnett, pribort, lexratet and Xt = euribort, lcommodityt, frat. For our second
benchmark specification: Yt = outputgaprealt, lnontradablet, tradablet, pribort, lexratet and Xt remains
the same.
The VAR specification in (1) represents a so-called reduced-form equation. In order to identify
the original shocks we can apply the recursiveness assumption by imposing restrictions on a
matrix linking the structural shocks to the reduced-form disturbances. The variables are ordered
in a specific way so as to represent the assumption that the monetary authorities choose the
interest rate taking into account the current level of prices and output (as in Mojon and
Peersman, 2001). In addition, the output gap and prices are assumed not to react immediately to
the monetary policy shock, but rather with a one-period lag. Mojon and Peersman (2001) follow a
recursive specification to analyze the impact of a monetary policy shock in some of the euro area
countries.
We analyze the sensitivity of our benchmark models first by using GDP instead of the output
gap, second by estimating a very parsimonious model without exogenous variables, and third by
estimating the baseline models by structural VAR instead of recursive VAR.
As regards the first sensitivity check, actual GDP data are used instead of the output gap. The
rationale for this exercise is that the output gap, as opposed to GDP, is unobservable. Our
second sensitivity check is motivated by degrees-of-freedom considerations. Here, we assume
that external shocks influence the Czech economy only via the exchange rate (i.e., B(L)=0).
Admittedly, this is a simplistic specification, but its main advantage is its limited number of
64 The inclusion of foreign variables that are considered exogenous is motivated by the need to control for foreign shocks and thus not to confuse domestic monetary shocks with the central bank’s responses to external developments (Jarocinski, 2006).
The Effects of Monetary Policy - 136 -
variables and thus its greater degree of freedom in comparison to our other models. As the third
robustness check, the two baseline models are estimated by structural VAR (SVAR). SVAR
represents an alternative identification scheme in order to recover the original residuals from the
reduced-form VAR. For structural VAR, we apply here the AB-model of Amisano and Giannini
(1997), which is defined as follows in a reduced form:
ttptt uXLBYLAY ++= − )()( ** (2),
tt BeAu 1−= , ( )Kt Ie ,0~ , where I is the identity matrix and K is the number of variables. A and
B are kk × matrices to be estimated. In the case of our first benchmark model, they are specified
as follows.
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
=
110
0010001
434241
3431
21
aaaaa
aA
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
=
44
33
22
11
000000000000
bb
bb
B
It follows from matrix A that a forward-looking monetary authority does not consider
contemporaneous prices while deciding on monetary policy (i.e., a32=0). However, monetary
authorities are likely to react to contemporaneous output (a31, as output can be regarded as an
excess demand pressure indicator) and exchange rate shocks (a34), which is a reasonable
assumption for small open economies according to Kim and Roubini (2000). More specifically,
exchange rate fluctuations influence the inflation forecast if they are deemed not to be transitory.
For our second benchmark model, in which we consider disaggregate prices (hence five
variables), matrices A and B look as follows:
The Effects of Monetary Policy - 137 -
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
=
1100
001000100001
54535251
4541
3231
21
aaaaaa
aaa
A
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
=
55
44
33
22
11
00000000000000000000
bb
bb
b
B
Following each VAR estimation, we perform stability checks in order to ensure the robustness of
our results (the results of these tests are available upon request). It is important to note that the
variables used in the VAR analysis do not need to be stationary. Sims (1980), among others,
argues against differencing even if the series contain a unit root. The main goal of the VAR
analysis is to analyze the co-movements in the data. What matters for the robustness of the VAR
results is the overall stationarity of the system (see Lütkepohl, 2006, for details).
A description of the FAVAR model is presented in Appendix 3.
4.5 Results
In this section, we discuss the estimated effects of Czech monetary policy within the
aforementioned specifications. The number of lags has been chosen according to the Schwartz
criterion and the parameter stability addressed by the CUSUM and CUSUM of squares tests and
the recursive coefficient estimation (the results are available upon request).
Figure 1 present our results regarding the effects of a contractionary monetary policy shock on
several economic variables of interest to a monetary authority. These figures contain the impulse
responses and the associated 95% confidence interval, which was bootstrapped using 1,000
replications according to the percentile method by Hall (1988).
The Effects of Monetary Policy - 138 -
Figure 1: Contractionary monetary policy shock, impulse responses
Notes: This figure shows the impulse responses to a one standard deviation contractionary monetary policy shock.
Time (on the horizontal axis) is measured in months.
We find that prices fall after a monetary tightening and bottom out after one year or so. This is in
line with the targeting horizon of the CNB, which is considered to be between 12 and 18
months. In terms of magnitude, our results show that a one Cholesky standard deviation of
interest rates (a 30 basis point monetary policy shock) decreases the log of prices by about 0.1%.65
Notably, there is essentially no evidence for a price puzzle.
The degree of economic activity, as measured by the output gap, falls after a contractionary
monetary policy shock, bottoming out after about four months (this, however, is not confirmed
in our sensitivity analysis, which identifies the bottom after about twelve months, which is more
sensible). The results indicate that a monetary shock of 30 basis points decreases the output gap
65 Several authors have raised the question of the accuracy of monetary policy shocks within VARs. See Boivin and Giannoni (2002) for a related discussion.
The Effects of Monetary Policy - 139 -
by about 5%. The responses of output and prices to a monetary shock show no support for the
cost channel of monetary policy.
Next, our results show a delayed overshooting in exchange rate behavior, i.e., a rather persistent
appreciation of the domestic currency after a monetary tightening (lasting typically about 6
months) and a gradual depreciation afterwards. However, it has to be pointed out that the
estimated confidence intervals are relatively wide, which brings some margins of uncertainty into
interpreting the results. Nevertheless, we can see that irrespective of specification and estimation
technique, the exchange rate depreciates over the longer term, which conforms to the uncovered
interest rate parity hypothesis (Kim and Roubini, 2000).
Figure 2: Contractionary monetary policy shock, impulse responses:
Tradable vs. non-tradable prices
Notes: This figure shows the impulse responses to a one standard deviation contractionary monetary policy shock.
Time (on the horizontal axis) is measured in months.
The Effects of Monetary Policy - 140 -
Figure 2 contains the estimates of the effect of monetary policy shocks on tradable and non-
tradable prices. Generally, tradable prices react faster than non-tradable prices to a monetary
contraction. While the bottom response of tradable prices is at one year or so (even 9–10
months), the bottom response of non-tradable prices occurs only after one and a half years. This
result matches the findings based on micro-level data (Alvarez and Hernando, 2006; Coricelli and
Horvath, 2006), which show that the frequency of non-tradable price changes is lower (and
negatively affected by the degree of competition); hence, a slower response to the monetary
policy shock is to be expected. On the other hand, the reaction of non-tradable prices is more
pronounced. A monetary shock of about 0.3% decreases tradable and non-tradable prices by
0.1% and 0.2%, respectively. In addition, the results in Figure 2 largely confirm the results of the
effect of monetary policy on output and the exchange rate from Figure 1.
Next, we analyze the sensitivity of our benchmark models, with all the results reported in
Appendix 2. First, we investigate how our results change when we include ex-post revised data
(GDP) instead of the real-time output gap in our data vector. Real-time variables are part of the
information set available at the time of policy-making, so by using these variables in the VAR
analysis we avoid the likely contamination of the results caused by data revisions.66 There is no
statistically significant reaction of GDP to the monetary shock and it seems that GDP does not
capture adequately the degree of demand pressures in an environment of sharply changing
potential output growth. Thus, our results stress the importance of using the real-time output gap
in the VAR specification, as it improves the precision of the empirical analysis.
Second, we estimate a very parsimonious model without exogenous variables, including the
forward-looking component. The rationale behind this is merely degrees-of-freedom
considerations. Interestingly, we find that a four-variable VAR is able to generate quite sensible 66 In general, the output gap should be a better measure of demand pressures (especially when potential output growth is changing), but one should keep in mind that it is unobservable and thus subject to greater uncertainty.
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and precisely estimated impulse responses.67 This would suggest that economic agents during our
sample period form their expectations in a rather backward-looking manner. This is somewhat
surprising, but one has to consider the transition process of the Czech economy and the
corresponding greater uncertainty in economic development, which could make agents rely more
on current data than on forecasts.
Finally, we also estimate the benchmark models by structural VAR instead of recursive VAR, but
SVAR seems to provide little value added and typically generates impulse responses close to
those of VAR, but with much larger confidence intervals.
Next, we compare our results with other recent studies that analyze monetary policy shocks in
the Czech Republic within the VAR approach. The comparison is summarized in Table 1. Most
of the existing studies ignore the monetary policy regime change in the Czech Republic (the fixed
exchange rate regime until May 1997 and the adoption of inflation targeting in January 1998). In
consequence, it is not surprising that simple VAR methods have difficulty in identifying monetary
policy shocks across these two regimes; i.e., they do not deliver plausible results and all exhibit
the price puzzle (some of them even report a positive reaction of output to a monetary
tightening).68 This suggests that the price puzzle in these studies is associated with the monetary
policy regime change. This is further confirmed by two papers that employ data from the
inflation targeting period (Elbourne and Haan, 2006, and this paper), as their results do not
67 The output gap and prices fall after a contractionary monetary policy shock, bottoming out after about twelve months. The exchange rate first appreciates, but later depreciates significantly, in line with uncovered interest rate parity (see also Eichenbaum and Evans, 1995). The results on the reaction of tradable and non-tradable prices largely comply with the benchmark case, except that non-tradable prices reach their bottom response a bit later (about two years). 68 The exemption is Jarocinski (2006). His sample starts in June 1997, which is before the adoption of inflation targeting, but after the exchange rate turbulence and the abandonment of the fixed exchange rate regime. As a result, we code his sample in Table 1 as coming from a single monetary policy regime. Another approach to dealing with monetary policy changes is presented by Darvas (2005), who estimates a time-varying coefficient VAR. Indeed, his results suggest that the values of the estimated parameters change rather abruptly around the year 1997 and remain relatively stable afterwards. (This is confirmed in this study by the recursive estimation of the parameters. The results are available upon request.)
The Effects of Monetary Policy - 142 -
exhibit the price puzzle. Finally, the results in Table 1 indicate that the bottom responses of
output and prices seem to be at around 4 quarters, which is in line with our findings.
Table 1: Comparison to other VAR studies on monetary transmission in the Czech
Republic
Sample period
Single monetary policy regime Estimation technique
Reaction of output to MP shock
Reaction of prices to MP shock
Bottom reaction of output and prices
EFN (2004) 1994–2003 No VAR (-), sig. (+), sig. 6Q/--- Ganev et al. (2004) 1995–2000 No VAR (+), n.a. (+), n.a. ---- Creel and Levasseur (2005) 1993–2004 No SVAR (+), sig. (+), sig. ---- Darvas (2005) 1993–2004 No TVC-SVAR (-), n.a. n.a. 4Q/n.a.Héricourt (2005) 1995–2004 No VAR (-), sig. (+), sig. 1Q/--- Hurník and Arnoštová (2005) 1994–2004 No VAR insig. insig. 8Q/6Q Elbourne and Haan (2006) 1998–2004 Yes SVAR (-), sig. (-), sig. 4Q/4Q Jarocinski (2006) 1997–2004 Yes Bayesian VAR (-), sig. (-), sig. 4Q/4Q Gavin and Kemme (2007) 1995–2006 No SVAR (-), sig. (+), sig. ---- Anzuini and Levy (2007) 1993–2002 No VAR, SVAR (-), sig. insig. 4Q/8Q This paper 1998–2006 Yes VAR, SVAR, FAVAR (-), sig. (-), sig. 3Q/4Q
Note: (-) and (+) denote, respectively, a statistically significant decline and increase of the variable after a monetary policy shock. The column “Single monetary policy regime” indicates whether the sample period of the study comes from a single monetary regime or spans different regimes (the fixed exchange rate regime until May 1997 and the inflation targeting regime adopted in January 1998). Abbreviations: TVC-SVAR – time-varying coefficient SVAR, Sig. – the reaction of the variable to a monetary policy shock is statistically significant at the 5% level, and Q – quarters. If the reaction of the variable to a monetary shock does not have the correct sign, the bottom reaction of the variable is not reported (denoted as “----” in the table; n.a. indicates that the corresponding estimates were not available in the original study).
4.6 Concluding Remarks
In this paper, we analyze the transmission of monetary policy shocks in the Czech Republic
within the VAR, SVAR, and FAVAR frameworks. In general, monetary transmission in the
Czech Republic seems to be similar, in terms of persistence of the responses of economic
variables to monetary shocks, to that in more developed countries, including the euro area (see
e.g. Mojon and Peersman, 2001).
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All in all, subject to various sensitivity tests, we find that prices and output decline after a
monetary tightening, with the bottom response occurring after about one year. This finding
corresponds with the actual targeting horizon of the Czech National Bank.69 In addition, we
document that the reaction of tradable prices is faster than that of non-tradable prices. While the
maximum effect of a monetary shock on tradables can be seen after a year or so, it is at least a
year and a half for non-tradable prices. This result broadly confirms the microeconomic evidence
on the effect of competition on price rigidity (Alvarez and Hernando, 2006; Coricelli and
Horvath, 2006). We avoid a price puzzle within the system. Thus, our results support the notion
that the price puzzle is associated with model misspecification rather than with the actual
behavior of the economy. This is also supported in other VAR studies on monetary transmission
in the Czech Republic, as all studies estimating the effects of monetary policy across different
monetary policy regimes (i.e., the fixed exchange rate regime and inflation targeting regime mixed
together) exhibit the price puzzle.
Next, there is a rationale for using the real-time output gap estimate instead of current GDP
growth, as using the former results in much more precise estimates. The impulse responses of
GDP to an interest rate shock are less precisely estimated, and thus our findings point to the
importance of real-time data in monetary policy analysis. Finally, our results also indicate a
persistent appreciation of the domestic currency after a monetary tightening (“delayed
overshooting”, Eichenbaum and Evans, 1995), although the confidence intervals are in this case
rather wide, with a gradual depreciation afterwards.
69 However, note that the targeting horizon (i.e., the horizon minimizing the loss function of the monetary authority) and the horizon at which the monetary policy impact is the most profound are not identical concepts. See Strasky (2005) for details.
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Appendix 1
Figure 3: Time series 3M PRIBOR
1Y Euribor
Output gap – real time
Log of GDP
Log of net price index
Log of CZK/EUR exchange rate
Log of tradable price index Log of non-tradable price index
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Forward rate agreements Log of commodity price index
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Appendix 2 – Additional Results:
Impulse Responses to a Monetary Shock
Figure 4: GDP instead of output gap
Figure 5: GDP instead of output gap, sectoral prices
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Figure 6: No exogenous variables
Figure 7: No exogenous variables, sectoral prices
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Figure 8: SVAR
Figure 9: SVAR, sectoral prices
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Appendix 3 – Factor-Augmented VAR
In this Appendix, we briefly document our attempt to study monetary policy effects within
factor-augmented VAR (FAVAR). However, as documented below, we find that the results
based on FAVAR are very sensitive and the confidence intervals for the impulse responses are
rather large.
We follow an approach developed by Bernanke et al. (2005).70 FAVAR can be represented in the
following form:
FY L
FY vt
t
t p
t pt
⎡
⎣⎢
⎤
⎦⎥ =
⎡
⎣⎢
⎤
⎦⎥ +
−
−Φ ( )
Vector Yt contains observable economic variables, whereas Ft represents unobserved factors
which provide additional economic information not fully captured by the Yt. We estimate these
unobservable factors using a principal components approach, which exploits the assumption that
information about the unobservable economic factors can be inferred from a large number of
economic time series Xt. Specifically, we can think of the unobservable factors in terms of
concepts such as “economic activity” or “investment climate.” They can be represented not by a
single economic variable, but rather by several time series of economic indicators.
The FAVAR methodology allows us not only to use a richer information set in the model
specification, but also to analyze the effects of a monetary policy shock on a greater number of
economic variables. There are two main approaches to estimating FAVAR: a two-step principal
components approach and a one-step approach that estimates (3) and a dynamic factor model
70 We followed the algorithm developed by Bernanke et al. (2005) to estimate FAVAR, which is available on the personal website of Jean Boivin: http://www2.gsb.columbia.edu/faculty/JBoivin/Personal/
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jointly. As Bernanke et al. (2005) do not find any particular differences between these two
estimators in terms of inference, we opt for the computationally simpler two-step approach.71
In our FAVAR specification, Xt consists of a balanced panel of 40 series that have been
transformed in order to ensure their stationarity. The description of these series and their
transformations is included in Table 2. The data is at monthly frequency and spans the period
from February 1998 to May 2006. Following Bernanke et al. (2005), we assume that the monetary
policy instrument (the 3-month interest rate) is the only observable factor, hence the only
variable included in Yt. For identification purposes the monetary policy instrument is ordered
last, which implies that latent factors do not respond contemporaneously (within a month) to
innovations in monetary policy. As in Bernanke et al. (2005), we distinguish between “slow-
moving” and “fast-moving” variables. A “slow-moving” variable is assumed not to react
contemporaneously to shocks, while the “fast-moving” variables react instantaneously to changes
in monetary policy or economic conditions. The classification of the variables into these two
categories is included in Table 2.
In the first step of the two-step estimation, we can distinguish three stages. First, we use principal
component analysis to estimate the common factors Ct from all the variables in Xt. Second, after
dividing the series in Xt into slow- and fast-moving ones, we estimate the “slow-moving” factors
stF)
as the principal components of the “slow-moving” variables. Finally, we estimate the
following regression:
tetYstFst YbFbC ++=))
71 See Bernanke et al. (2005) for a more detailed discussion of principal component analysis and FAVAR.
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Based on these estimates, tF)
is constructed as tYt YbC))
− . In the second step, we estimate the
VAR in tF)
and Yt, using a recursive assumption.
One caveat in our analysis is the fact that we have only 40 series available for principal
component analysis, as compared to the 120 used in Bernanke et al. (2005). While this may be
viewed as a weakness at first sight, it has been argued by Boivin and Ng (2006) that more series
do not necessarily ensure better data quality, due to cross-correlation of idiosyncratic errors. In
one of their tests, they show that the factors extracted from 40 pre-screened series may in some
cases yield better results as compared to using 147 series. Therefore, for the sake of size, 40
series, at least in general, should not pose a problem.
Our main results are given in Figure 9. Each panel shows the impulse responses of selected
macroeconomic variables to a monetary policy shock with 90% confidence intervals. The
FAVAR model in Figure 9 includes three principal factors, but the results were no different when
the number of factors was changed. In the benchmark specification we use one lag. The results
are highly sensitive to the numbers of lags used, with more lags resulting in highly improbable
results.
As a result, the FAVAR model does not appear to properly capture the developments in the
Czech economy. Most importantly, the confidence intervals are too large to infer the direction of
the impact of the monetary policy change on the macroeconomic variables. The exception is
actual GDP growth, lgdpt, which declines after monetary policy shock, as predicted by the theory.
There may be several reasons for the lack of significant results in the FAVAR estimation for the
Czech economy. One reason is likely to do with the relatively short span of the available data;
The Effects of Monetary Policy - 155 -
another may be data quality, as discussed by Boivin and Ng (2006). As it is at monthly frequency,
our dataset lacks variables related to consumption, housing starts and sales as well as real
inventories and therefore some important economic information may be missing.
Figure 9: FAVAR results
Note: Impulse responses with 90% confidence intervals are presented.
Table 2 – Data description
VAR DESCRIPTION TRANSFORMATION SOURCE
Real output and income var1* Industrial Production, Index number (sa) 3 IFS var10* Construction output, constant prices - % (sa) 1 ARAD
var11* Contracted construction work in enterprises with 20 employees or more - constant prices - (%) (sa) 1 ARAD
var20* Outputgap real - interpolated from quarterly values 2 ARAD var21* GDP - interpolated from quarterly values (sa) 3 ARAD var30 Total agricultural goods output (sa) 3 ARAD Employment and Hours var2* Industrial Employment (sa) 3 IFS var3* Unemployment Rate (sa) 1 Eurostat
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var12* Registered job applicants, total (thousand persons, sa) 1 ARAD var13* Vacancies (thousand, sa) 3 ARAD var14* Newly registered job applicants (thousand persons, sa) 3 ARAD var15* Registered job applicants on unemployment benefit (thousand persons, sa) 3 ARAD Industry Sales
var6* Total sales revenues, Index sales in industry-constant price (corresponding period of preceding year=100, sa) 3 ARAD
var7* Mining and quarrying, Index sales in industry-constant price (corresponding period of preceding year=100, sa) 1 ARAD
var8* Manufacturing, Index sales in industry-constant price (corresponding period of preceding year=100, sa) 1 ARAD
var9* Electricity, gas and water supply, Index sales in industry-constant price (corresponding period of preceding year=100, sa) 1 ARAD
Exchange Rates var22 Foreign Exchange Rate (Czech Krown per Euro) 3 IFS var23 Foreign Exchange Rate (Czech Krown per U.S. $) 3 IFS Interest Rates var26 Treasury Bill Rate 3 IFS var27 Deposit Rate 3 IFS var28 Lending Rate 3 IFS var29 Government Bond Yield 3 IFS var31 1 day Interbank Rate PRIBOR (%) 1 ARAD var32 7 day Interbank Rate PRIBOR(%) 1 ARAD var33 14 day Interbank Rate PRIBOR(%) 1 ARAD var34 1 month Interbank Rate PRIBOR(%) 2 ARAD var35 2 month Interbank Rate PRIBOR (%) 2 ARAD var36 6 month Interbank Rate PRIBOR(%) 2 ARAD var37 9 month Interbank Rate PRIBOR (%) 2 ARAD var38 1 year Interbank Rate PRIBOR(%) 2 ARAD var41 3 month Interbank Rate PRIBOR(%); monetary policy instrument 1 ARAD Money Aggregates var24 Money (sa) 3 IFS var25 Money plus Quasi Money (sa) 3 IFS Price Indexes var16* Consumer Prices CPI (sa) 3 ARAD var17* Industrial Produces Prices (sa) 3 ARAD var18* Tradable prices (sa) 3 ARAD var19* Nontradable prices (sa) 3 ARAD
var40 Prague Stock Exchange Index PX50, Historical close, average of observations through period 3 IFS
Exports and Imports var4* Exports 3 IFS var5* Imports, FOB 3 IFS
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All series were tested for unit root and when necessary were transformed to achieve stationarity. The transformation codes are: 1-no transformation, 2-first difference, and 3-first difference of logarithm. An asterisk (*) next to the mnemonic indicates a variable assumed to be “slow-moving” in the estimation.