WORKING PAPER 208

Martin Feldkircher, Florian Huber

Unconventional US Monetary Policy: New Tools Same Channels?

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Unconventional US Monetary Policy: New ToolsSame Channels?∗

Martin Feldkirchera and Florian Huberb

aOesterreichische Nationalbank (OeNB)bVienna University of Economics and Business (WU)

Abstract

In this paper we compare the transmission of a conventional monetary policyshock with that of an unexpected decrease in the term spread, which mirrorsquantitative easing. Employing a time-varying vector autoregression withstochastic volatility, our results are two-fold: First, the spread shock worksmainly through a boost to consumer wealth growth, while a conventionalmonetary policy shock affects real output growth via a broad credit / banklending channel. Second, both shocks exhibit a distinct pattern over oursample period. More specifically, we find small output effects of a conventionalmonetary policy shock during the period of the global financial crisis andstronger effects in its aftermath. This might imply that when the centralbank has left the policy rate unaltered for an extended period of time, apolicy surprise might boost output particularly strongly. By contrast, thespread shock has affected output growth most strongly during the period of theglobal financial crisis and less so thereafter. This might point to diminishingeffects of large scale asset purchase programs.

Keywords: Unconventional monetary policy, transmission channel,Bayesian TVP-SV-VAR.

JEL Codes: C32, E52, E32.

∗The views expressed in this paper are not necessarily those of the Oesterreichische National-bank. We would like to thank Alessandro Galesi, Lukas Reiss, Fabio Rumler and participants ofthe 9th International Conference on Computational and Financial Econometrics (CFE 2015) andresearch seminars at the OeNB and the Vienna University of Business and Economics for helpfulcomments. Email: martin.feldkircher@oenb.at and florian.huber@wu.ac.at. Correspondingauthor: Martin Feldkircher, Oesterreichische Nationalbank (OeNB), Phone: +43-1-404 20-5251.The opinions expressed in this paper are those of the authors and do not necessarily reflect theofficial viewpoint of the Oesterreichische Nationalbank or the Eurosystem.

Non-technical summary

The severity of the global financial crisis and the inability to generate a turnaround

with ordinary policy tools stimulated the use of other policy measures to spur eco-

nomic growth. One such measure is quantitative easing (QE). In 2008, the US

Federal Reserve (Fed) started buying longer term securities with the objective of

increasing the amount of liquid assets in the economy and pushing down interest

rates.

In this paper, we analyze the transmission of quantitative easing in the USA

and compare this to the transmission mechanism of conventional monetary policy.

The former is assumed to work through a lowering of the term spread (given short-

term rates are zero), whereas the latter is modeled by a decrease in the policy rate.

The econometric framework we propose explicitly accounts for an important facet

of monetary policy, namely that its strength – and more broadly the transmission

mechanism – might depend on the current economic environment and is thus time

dependent. For example, stimulus from a monetary policy shock might be differ-

ent during recessions, where the economy undergoes balance sheet adjustments and

heightened uncertainty weighs on the overall business climate, expansions and nor-

mal times. The same holds true for unconventional monetary policy.

Our results are as follows: First, and looking at the transmission channels, we find

that lowering the term spread affects aggregate demand by an increase in consumer

wealth. By contrast, conventional monetary policy works mainly through a broad

credit channel. Second, we find a pronounced and distinct pattern of the effectiveness

of conventional and unconventional monetary policy over time. More specifically, a

monetary policy shock is less effective in stimulating inflation and output growth

during the period of the global financial crisis, whereas the opposite holds true for

lowering the term spread. The latter is particularly effective during the crisis period

where overall uncertainty was high and the Fed’s engagement in quantitative easing

served as an important signal to longer-term accommodative monetary policy. In the

aftermath of the crisis when the policy rate was effectively zero, this pattern reverses

and a hypothetical monetary policy shock would boost output growth particularly

strongly. Taken at face value, this finding suggests that a correction of the monetary

policy stance after an extended period of unchanged monetary policy might have

large macroeconomic effects.

1 Introduction

With the onset of the global financial crisis the US Federal Reserve (Fed) beganto lower interest rates to stimulate the economy. Since December 2008, however,the Federal funds rate (FFR) is effectively zero, leaving no room for conventionalmonetary policy to further enhance economic growth. Against the backdrop oflackluster economic conditions and the perceived risks of deflation at that time, theUS Fed decided to engage in ”unconventional” monetary policy which took mostlythe form of asset purchases from the private banking and non-banking sector. Afterthree large scale asset purchase programs (LSAPs), assets on the central bank’sbalance sheet more than quadrupled since 2007 to about 4,500 billion US dollar inFebruary 2015.

While a large body of empirical literature has hitherto investigated how conven-tional US monetary policy affects the real economy, there is scant empirical evidenceon the transmission of quantitative easing (QE). QE implies switching from interestrate targeting steered via reserve management to targeting the quantity of reserves(Fawley and Juvenal, 2012). In the USA, the Fed did so by buying longer-term se-curities either issued by the US government or guaranteed by government-sponsoredagencies. This should directly put downward pressure on long-term yields in thesemarkets. In addition, financing conditions will ease more generally, since investorsselling to the Fed reinvest those proceeds to buy other longer-term securities such ascorporate bonds and other privately issued securities (portfolio re-balancing, Joyceet al., 2012). On the back of increased equity prices and heightened loan demand,both private sector wealth and asset growth in the banking sector should tick up,leading to an increase in aggregate demand.

The strength of these transmission channels is likely to depend on the currenteconomic environment. In fact, and considering the transmission of conventionalmonetary policy, several authors have suggested that the transmission mechanismhas changed over time (see e.g., Boivin and Giannoni, 2006; Boivin et al., 2010). Thismight hold especially true for the most recent past that includes the global financialcrisis marking a severe rupture of the financial system and the way how monetarypolicy is conducted. Arguments why a monetary policy shock might have smallereffects during recessions associated with financial crises such as the one in 2008/09include balance sheet adjustments and deleveraging in the private sector, which typ-ically takes place after economic boom phases that predate financial crises (Bech etal., 2014). Also heightened uncertainty might weigh on business climate and impedeinvestment growth. Aastveit et al. (2013) and Hubrich and Tetlow (2014) investi-gate monetary policy in times of financial stress or heightened uncertainty and findsmaller effects in these periods for the USA and Tenreyro and Thwaites (2013) findmore generally that US monetary policy is less effective during recessions. Whetherthese arguments carry over to unconventional monetary policy is less researched.Recent work actually suggests the opposite. For example, Engen et al. (2015) em-phasize the role of quantitative easing in underpinning the commitment of the Fedto be accommodative for a longer period. This signaling channel is more effectivewhen financial markets are impaired and economic conditions characterized by highuncertainty. This reasoning ascribes quantitative easing the greatest effectiveness

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during the onset of a crisis, contrasting the empirical work on the effectiveness ofconventional monetary policy during financial crises. In a recent paper, Wu (2014)corroborate this result attesting the latest asset purchase programs a smaller effectthan the earlier ones.

In this paper we address these questions within a coherent econometric frame-work. More specifically, and to cover a broad range of potential transmission chan-nels, we propose a simple Bayesian estimation framework that handles medium- tolarge-scale models, allows for drifting parameters and time-varying variances andcovariances. Akin to Baumeister and Benati (2013), we model the asset purchasesof the US Fed by assuming a compression of the yield curve. The transmission ofthe ”spread shock” is compared with that of a conventional monetary policy shock.

Our main results can be summarized as follows: First, we find evidence thatunconventional monetary policy works mainly via the wealth channel to spur aggre-gate demand. There is less evidence for the credit / bank lending channel. Second,conventional monetary policy works strongly through expanding assets and depositsof the banking sector, while the impact on consumer wealth growth is more modest.Last, for both shocks we find a distinct pattern over our sample period. More specif-ically, we find small output effects of a conventional monetary policy shock duringthe period of the global financial crisis and stronger effects in its aftermath. Thismight imply that when the central bank has successfully committed the policy rateto a certain value, an unexpected deviation from that commitment might boost out-put growth particularly strongly. By contrast, the spread shock has affected outputgrowth most strongly during the period of the global financial crisis, when the Fedlaunched its first asset purchase program and less so thereafter. This might pointto diminishing effects of large scale asset purchase programs on real output growth.

The paper is structured as follows. Section 2 introduces the econometric frame-work and how we identify the monetary policy and the term spread shock. Section 3investigates the effects and the transmission of the two shocks over time, while Sec-tion 4 concludes.

2 Econometric framework

In this section we introduce the data, the econometric framework and the identifi-cation strategy to investigate the transmission of unconventional and conventionalmonetary policy. We use a novel approach to estimation based on work by Lopes etal. (2013) that can handle medium- to large scale time-varying vector autoregressionswith stochastic volatility (TVP-SV-VAR).

2.1 Data

Our analysis is based on variables typically employed in monetary vector autoregres-sions and on quarterly frequency. The time period we consider spans from 1984Q1to 2015Q1 and the variables comprise real GDP growth (∆gdp), consumer priceinflation (∆p), the federal funds rate (is) and the term spread (sp) defined as theyield on 10-year-government bonds minus the Federal Funds rate. In addition tothese standard variables, we include several variables that should allow us to gaugethe importance of different channels for monetary policy transmission. These are

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growth in net household and non-profit organizations’ wealth (∆wealth), growth incommercial banks’ assets and deposits (∆banks assets,∆banks deposits) and thenet interest rate margin (nim) of large US banks. Growth rates are calculated aslog-differences and are thus in quarter-on-quarter terms.1

2.2 The TVP-SV-VAR model with a Cholesky structure

In what follows we draw on a new approach to estimate a TVP-SV-VAR. This ap-proach differs to standard estimation by recasting the VAR as a system of unrelatedregressions and imposing a recursive structure on the model a priori.

We collect the data in an m = 8× 1 vector

yt = (∆gdpt,∆pt,∆wealtht, is,t,∆banks assetst,∆banks depositst, spt, nimt)′.

Now, we assume the individual elements of yt to be described by a set of equa-tions, with the first equation i = 1 given by

y1t = c1t +

p∑j=1

b1j,tyt−j + e1t (2.1)

e1t ∼ N (0, λ1t) (2.2)

and for i = 2, . . . ,m

yit = cit +i−1∑s=1

ais,tyst +

p∑j=1

bij,tyt−j + eit (2.3)

eit ∼ N (0, λit) (2.4)

where cit (i = 1, . . . ,m) denotes a constant and b′ij,t (j = 1, . . . , p) are m-dimensionalcoefficient vectors associated with the p = 2 lags of yt in each equation. The trian-gular structure is imposed on the contemporaneous coefficients. More specifically,the ais,t denote coefficients associated with the first i−1 elements of yt with a1s,t = 0for s = 1, . . . , i − 1. Finally, eit is a normally distributed error with time-varyingvariance given by λit. Note that all coefficients in Eqs (2.1)- (2.4) are allowed tovary over time.

We assume that ais,t evolves according to

ais,t = ais,t−1 + uit for i = 2, . . . ,m. (2.5)

1Data on real GDP growth (GDPC96), CPI inflation (CPALTT01USQ661S), the effective fed-eral funds rate (FEDFUNDS) calculated as the quarterly average of daily rates, 10-year-governmentbond yields to proxy long-term interest rates (IRLTLT01USQ156N), net worth of householdsand nonprofit organizations resembling consumer wealth (TNWBSHNO) deflated by the per-sonal income deflator (PCECTPI) and net interest rate margins for large US banks (USG15NIM)are from the Fred data base, https://research.stlouisfed.org/fred2/. Data on commer-cial banks’ assets (FL764090005.Q, FL474090005.Q), deposits (FL763127005.Q, FL764110005.QFL763131005.Q, FL763135005.Q,FL762150005.Q) are from the financial accounts data base of thefederal reserve system, http://www.federalserver.gov/releases/z1/about.htm.

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uit is a standard white noise error term with variance σ2i . Equation (2.5) implies that

the parameters associated with the contemporaneous terms are following a randomwalk.

Let us define amp-dimensional vector bit = (bi1,t, . . . , bip,t)′. Similarly to Eq. (2.5)

we assume that bit follows the subsequent law of motion

bit = bit−1 + vit. (2.6)

with vit being a vector white noise error with variance-covariance matrix equal toQi. Finally, the λits follow

hit = µi + ρi(hi,t−1 − µi) + ηit for i = 1, . . . ,m, (2.7)

where hit = log(λit) denotes the log-volatility, µi is the mean of the log-volatilityand ρi ∈ (−1, 1) the autoregressive parameter. ηit is the zero-mean error termwith variance ς2

i . Several studies have shown that it is important to allow for bothchanges in residual variances and parameters. Assuming constant error variances,while they are in fact time-varying, could lead to misleading parameter estimates ofthe VAR.2 Moreover, changes in the economic environment can affect how monetarypolicy transmits to the real economy. In other words, previous literature suggestedthat the volatility of economic shocks also tends to influence real activity (Bloom,2009; Fernandez-Villaverde et al., 2011).

The reason why the log-volatility process is assumed to be stationary in contrastto the non-stationary state equation of the autoregressive parameters is mainly dueto the fact that a random walk assumption for the log-volatility would imply that itis unbounded in the limit, hitting any lower or upper bound with probability one.In practice, however, the differences between a stationary and non-stationary stateequation is negligible since the data is not really informative about the specific valueof ρi.

3

The model given by Eq. (2.1) - Eq. (2.4) can be recast in a more compact formby collecting all contemporaneous terms on the left-hand side

Atyt = ct +

p∑j=1

Bjtyt−j + et (2.8)

where At denotes a m × m lower triangular matrix with diagonal diag(At) = ιmand the typical non-unit/non-zero element given by −asj,t. Here, we let ιm be am-dimensional unit vector. In what follows we collect free elements of At in am(m− 1)/2 vector at. ct is a m× 1 vector of constants and Bjt = (b′1j,t, . . . , b

′mj,t)

′

denotes a m×m dimensional coefficient matrix to be estimated. The m-dimensionalerror vector has zero mean and a diagonal time-varying variance-covariance matrixgiven by Λt = diag(λ1t, . . . , λmt). Equation (2.8) resembles the structural TVP-SV-VAR model put forth in Primiceri (2005). The lower triangular nature of At is

2See for example, Cogley and Sargent (2005), who in response to criticism raised by Sims (2001),extend their TVP framework put forward in Cogley and Sargent (2002) to allow for stochasticvolatility.

3In fact, experimenting with stationary state equations for ait and bit leaves our results qual-itatively unchanged.

4

closely related to a recursive identification scheme, which assumes a natural orderingof variables. In fact we use the ordering as the variables appear in yt. However, notethat we do not identify the shocks based on this Cholesky decomposition. Ratherwe impose the triangular structure due to computational reasons only, while identi-fication of the shocks will be based on sign restrictions discussed in subsection 2.4.These are two isolated steps and the a priori Cholesky decomposition does not inter-fere with identification based on sign restrictions, which re-weights orthogonalizederrors (that we directly obtain from the estimation stage of the model) and selectsthose that fulfill the postulated sign restrictions. Our structural analysis will thusbe unaffected by the triangular structure imposed on the model. For an excellentoverview on sign restrictions see Fry and Pagan (2011). In subsection 3.4 we showthat estimates based on a different ordering yield virtually the same impulse responsefunctions.

In the absence of specific assumptions on At, the model in Eq. (2.8) is notidentified. Thus, researchers usually estimate the reduced form imposing restrictionsthat originate from theory ex-post.4 The reduced form of the TVP-SV-VAR is givenby

yt = dt +

p∑j=1

F jtyt−j + ut (2.9)

with dt = A−1t ct, F j = A−1

t Bj and ut = A−1t et. The reduced form errors ut are

normally distributed with variance covariance matrix given by Σt = A−1t Λt(A

−1t )′.

It can easily be seen that the matrix At establishes contemporaneous links betweenthe variables in the system.

To emphasize the distinct features of our estimation strategy, it is worth men-tioning how this model is traditionally estimated. Typically, one would start withthe complete system of reduced form equations given in Eq. (2.9) and obtain re-duced form parameter estimates by employing Gibbs sampling coupled with a dataaugmentation scheme (Primiceri, 2005; Cogley et al., 2005). This approach to esti-mation comes along with a significant computational burden. To be more precise, ifas in our case m = 8 and the number of lags is set to p = 2, the algorithms outlinedin Carter and Kohn (1994) and Fruhwirth-Schnatter (1994) require the inversion ofa k×k variance-covariance matrix at each point in time. In our case k = m(mp+1)would be k = 136 rendering estimation with the traditional algorithms cumber-some.5

Following Lopes et al. (2013), we impose a Cholesky structure a priori, estimatethe structural form in an equation-by-equation fashion, use the estimated coefficientsto solve Eq. (2.8) to finally obtain Eq. (2.9). Using an equation-by-equation approachdecreases the computational burden significantly, by first reducing the dimension ofthe matrices that have to be inverted. More specifically, while the inversion of a k×kmatrix requires m3(mp+1)3 operations using Gaussian elimination, we reduce this to

4For notable exceptions see, among others, Sims and Zha (1998) and Baumeister and Hamilton(2015).

5Another strand of the literature proposes factor augmented VARs (FAVARs) with driftingparameters and stochastic volatility (Korobilis, 2013). While FAVARs provide a flexible means ofreducing the dimensionality of the estimation problem at hand they could also lead to problemswith respect to identification and structural interpretation of the underlying shocks.

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m(mp+ 1)3, which is a marked gain as compared to full-system estimation. Second,and more importantly, equation-by-equation estimation can make full use of parallelcomputing. Recently, Carriero et al. (2015) suggested a related estimation strategy,which imposes a triangular structure on the errors rather than the contemporaneouscoefficients related to the dependent variable. While this approach is invariant to theordering of the variables it prohibits parallel computing and hence computationalgains are more limited.

2.3 Bayesian Inference

We use a Bayesian approach and impose tight priors on the variance-covariancestructure in the various state equations, which describe the law of motion for theparameters.

General prior setup and implementation

Following Primiceri (2005) and Cogley et al. (2005) we impose a normally distributedprior on the free elements of the initial state At which are collected in a vector a0

and on b0 = vec(Bj0)

a0 ∼ N (a0,V a), (2.10)

b0 ∼ N (b0,V b), (2.11)

where a0 and b0 are prior mean matrices and V a and V b are prior variance-covariance matrices. We follow common practice (Primiceri, 2005) and use a trainingsample of T = 30 quarters to scale the priors. We set the prior mean for a0 andb0 equal to the OLS estimate based on this training sample. The prior variance-covariance matrices are specified such that V a = 4 × V a and V b = 4 × V b, withV a and V b being the variances of the OLS estimator.6

The priors on the variance-covariances in the state equations (2.5) and (2.6) areof inverted Wishart form:

S ∼ IW(vS,S), (2.12)

Q ∼ IW(vQ,Q), (2.13)

with S denoting the variance-covariance matrix of at. This matrix is block-diagonalwith each block corresponding to the m equations of the system. The degree offreedom parameters are denoted by vS and vQ and the corresponding prior scaling

matrices are labeled as S and Q. In principle we set vS = vQ = T and S = k2S× V a

with kS being a scalar parameter controlling the tightness on the propensity ofat to drift. We set k2

S = 0.01 after having experimented with a grid of differentvalues. The results remain qualitatively unchanged as long as the prior is not settoo loose, placing a lot of prior mass on regions of the parameter space which implyexplosive behavior of the model. We use the same hyperparameters for the prior onQ, i.e., vQ = T and Q = k2

b × V b with k2b = 0.01. Again, this choice is based on

6Since we estimate the model on an equation-by-equation basis V a and V b are block diagonalmatrices.

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experimenting with a grid of values ruling out hyperparameter choices that implyexcessively explosive behavior of the model.

We impose the following prior setup on the parameters of Eq. (2.7)

µi ∼ N (µi, V µ) (2.14)

ρi + 1

2∼ Beta(γ0, γ1) (2.15)

ς2i ∼ G(1/2, 1/2Bσ). (2.16)

Finally, we follow Kastner and Fruhwirth-Schnatter (2013) and set µi

= 0 andV µ = 10, implying a loose prior on the level of the log-volatility. The prior on ρi isset such that a lot of prior mass is centered on regions for ρi close to unity, providingprior evidence for non-stationary behavior of hit. Thus we set γ0 = 25 and γ1 = 1.5.For the non-conjugate Gamma prior on ς2

i we set Bσ equal to one. The appendixcontains a brief sketch of the Markov Chain Monte Carlo (MCMC) algorithm toestimate the model.

2.4 Structural identification

To identify a US monetary policy shock and a shock to the term spread we use aset of sign restrictions put directly on the impulse responses. More specifically, weidentify a ”monetary policy” or ”term spread” shock by singling out from a set ofgenerated responses those that comply with our a priori reasoning how the economytypically responds to either of the shocks. The restrictions refer to the directionalmovements of impulse responses on impact and are outlined in Table 1.

[Table 1 about here.]

We look at two monetary policy shocks and three broad transmission channels.We assume that an expansionary conventional monetary policy shock works via anunexpected lowering of the short-term interest rate. The most direct way lowerinterest rates feed into the economy is via the ”interest rate / investment” channel.The decrease in the policy rate lowers the user cost of capital thereby spurringinvestment and real GDP growth (Ireland, 2005). In addition, aggregate demand canalso increase through a boost to ”consumption wealth” as advocated in Ludvigsonet al. (2002). Following a monetary expansion, equity prices are likely to tick upsince the price of debt instruments rises in parallel with the reduction of the short-term rate making them less attractive for investors (Ireland, 2005). This leads to anincrease in consumer wealth which might boost consumption spending and aggregatedemand (Ludvigson et al., 2002).

The cut in short-term interest rates has also bearings on the financial side ofthe economy. We assume an increase in the term spread in response to a decreaseof the policy rate. This can be motivated by an imperfect pass-through along theterm structure implying that long-term interest rates do not follow the decreasein short-term interest rates one-to-one (Baumeister and Benati, 2013).7 Trailing

7More specifically, an unexpected monetary expansion can be expected to drive up inflationand therefore inflation expectations. This in turn implies long-rates to decrease less strongly thanshort rates causing a widening of the yield curve (Benati and Goodhart, 2008).

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the term spread, net interest rate margins of banks tend to increase (Adrian andShin, 2010). This affects asset and deposit growth of the banking sector along twodimensions. First, the decrease in the long-term rate (even if less pronounced thanthat of short-rates) makes taking a loan cheaper, implying that the demand forloans is strengthened by the policy induced decrease of the short-term rate. Thiseffect is amplified by an improvement of balance sheets of households and firms onthe back of the policy induced rise of asset prices, which increases the demand forloans by those that were previously excluded from access to credit (”balance-sheetchannel”). Second and since net interest rate margins increase, generating new loansbecomes more attractive for banks (compared to faring excessive reserves with theFed). Thus, the supply for loans is stimulated as well. As a consequence, depositgrowth is assumed to tick up. The newly generated loans will increase depositsmechanically since for each newly issued loan the bank creates a deposit of thesame amount. On top of that, the increase of reservable deposits created by themonetary expansion will reduce the amount of managed liabilities banks need tofund their loans. This might be passed on to their clients by lowering loan ratesand increasing loan supply (Bernanke and Blinder, 1988; Black et al., 2007). Wesummarize these developments under a broad ”credit and bank lending channel”.Naturally, aggregate demand is positively affected by loan growth which leads tomore investment and consumption.

Second, we investigate a shock to the term spread. Since the purchases of longer-term securities has significantly lowered longer-term yields – as demonstrated e.g.,in Doh (2010), Gagnon et al. (2011), Krishnamurthy and Vissing-Jorgensen (2011),and Hamilton and Wu (2012) – assuming a reduction of the term spread, can bethought of a way to model the effects of quantitative easing within a standardmonetary VAR framework. In contrast to a conventional expansionary monetarypolicy shock, asset purchases by the central bank will trigger a decrease in the termspread. As with the monetary policy shock, a shock to the term spread will trig-ger an increase in equity prices since yields on debt securities decline. An increasein consumer wealth, coupled with eased finance conditions should spur economicactivity and inflation. That asset purchase programs had an effect on consumerconfidence through signaling has been emphasized in Engen et al. (2015) and Wu(2014). While we can investigate the signaling channel implicitly by tracing the ef-fectiveness of unconventional monetary policy through periods of different financialand economic conditions, we cannot model this transmission mechanism explicitlyby including a suitable control variable. Looking at the financial side of the econ-omy, the reduction of the term spread triggers a decrease in net interest margins ofcommercial banks: since the cost of funding (the short-term interest rate) is unal-tered and tied to the zero lower bound, the revenues of lending (approximated bythe long-term interest rate) decreases. As in Adrian and Shin (2010) this impliesan inward shift of the supply curve of credit and is likely to contain new lending.This effect, however, might be offset by a stronger demand for lending, since lowerlong-term rates make it more attractive to take a loan. Since a priori we do notknow which of these effects is likely to dominate, we leave the signs on growth inbank assets unrestricted. Next and in line with the assumption about the mone-tary policy shock, we assume an initial increase in banks’ deposits. This increase

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is rather mechanical since the proceeds of the asset purchase will be deposited inthe investors’ banks’ accounts raising deposits of the banking sector and might berather short-lived as pointed out in Butt et al. (2014).8

Last and to mimic the current monetary policy environment in the USA withthe federal funds rate standing technically at the zero lower bound, we will hold theresponse of the short-term interest rate constant at zero for eight quarters (Baumeis-ter and Benati, 2013). Note that this is unrelated to identification of the shock, forwhich restrictions are only binding on impact. The appendix provides further detailson the technical implementation of the sign restrictions and the zero restriction onthe short-term interest rate for the spread shock.

3 Empirical results

In this section we investigate the transmission of the monetary policy and the termspread shock, examine whether overall effects vary over time and establish that bothshocks mattered historically in determining fluctuations in the time series consideredin this paper. We start by briefly summarizing the movements of the two identifiedshocks over time. This should yield further confidence regarding the appropriatenessof the proposed restrictions to recover the shocks. Figure 1 shows the structuralshocks, left panel relating to the term spread shock, mid panel to the monetarypolicy shock. For completeness we also show the evolution of the actual FederalFunds Rate and the term spread in the right panel.

[Fig. 1 about here.]

Looking at the term spread shock first, we have indicated three distinct timeperiods by red vertical bars, namely the start of the Clinton debt buy back program(Q1 2000 to Q4 2001), which was in many ways similar to an LSAP, and the startof LSAPs I to III (Q4 2008, Q4 2010 and Q3 2012, see Dunne et al., 2015). Thefigure shows that negative surprises to the term spread indeed coincide with theseperiods. There is also a pronounced negative shock visible in the last quarter of2003 in which the term spread started to decrease sharply (see right panel, Fig. 1).

The monetary policy shock is shown in the mid panel. For comparison we alsoplot a monetary policy shock series based on the narrative approach put forwardin Romer and Romer (2004) extended to cover the period up until Q4 2008.9 Bothshocks identify the same monetary policy cycle and the correlation between theseries amounts to about 0.6.

8In case the Fed purchases assets directly from the banking sector, the proceeds would becharged to the banks’ reserve balances with the Fed, leaving deposits untouched. The positiverestriction on deposit growth is warranted since part of the Fed’s purchases directly concern theprivate non-banking sector.

9To be precise, the narrative shock is transformed to quarterly frequency by simply averagingover the corresponding months. The monetary policy shock corresponds to the smoothed structuralshocks. In general, residuals of the VAR are more volatile due to the inherent iid assumption, whichis why we opted for smoothing the shocks facilitating visual comparison to the more persistentnarrative shocks.

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3.1 How do term spread and monetary policy shocks affect outputgrowth and inflation?

In this section we examine through which channels both shocks affect aggregatedemand and CPI inflation. To this end, we report impulse response functions infigures 2 and 3 and a related forecast error variance decomposition in Table 2.Since we use a time-varying framework, reported impulse responses show how theeconomy would react to a hypothetical shock at a specific point in time. Thisholds equally true for sample periods where actually no monetary policy / spreadshock occurred. All results are based on 500 draws from the full set of 15,000posterior draws that have been collected after a burn-in phase of 15,000 draws.Both shocks are normalized to a 100 basis points (bp) reduction either of the policyrate (monetary policy shock) or the term spread (spread shock). Results are shownfor real GDP growth, inflation, wealth growth and banking sector variables.

The top panel of Fig. 2 lists results for real output growth, on the left-hand inresponse to the conventional monetary policy shock and on the right-hand side inreaction to the term spread shock. Note that we have opted for slicing the time-varying impulse responses by fixing time periods of interest to show accompanyingcredible sets (50% in dark blue and 68% in light blue). These periods relate to theglobal financial crisis, namely the pre-crisis period (1991Q1 to 2007Q3), the crisisperiod (2007Q4 to 2009Q2) and its aftermath (2009Q3 to 2015Q1).10

[Fig. 2 about here.]

[Table 2 about here.]

Looking at the unexpected lowering of the policy rate first, we find positiveand tightly estimated responses up until eight quarters, indicating rather persistenteffects on output growth. This holds true throughout the sample periods considered.The size of the effects, however, varies with the period under consideration. Morespecifically, the 100 bp decrease in the policy rate accelerates real GDP growth onimpact by around 0.3 to 0.4 percentage points prior to and during the crisis. In theaftermath of the global financial crisis this effect increases markedly to about 0.7percentage points.11 To put our results into perspective, we compare the cumulativeresponses with established findings of the literature, that are mainly based on pre-crisis data. In cumulative terms, the responses prior to the crisis point to an increasein real GDP by 1.8%, whereas previous findings indicate peak level effects of about0.3% to 0.6% (see e.g., Leeper et al., 1996; Bernanke et al., 1997; Uhlig, 2005). Ina more recent paper, Gorodnichenko (2005) reports a peak effect in real GDP ofapproximately 0.8%. See Coibion (2012) for an excellent and more comprehensivesummary of the relevant literature.

Responses of output growth to the lowering of the term spread are depicted onthe right-hand side of the top panel of Fig. 2. The term spread shock accelerates

10These are based on the NBER dating of recessions, available at http://www.nber.org/

cycles.html. The full history of impulse responses over time and for all variables is availablefrom the authors upon request.

11Responses are to be interpreted as the reaction of a variable to a hypothetical 100 bp monetarypolicy / term spread shock independent of the actual value of the FFR during that period.

10

real GDP growth throughout the sample period. Our estimates are broadly in linewith those provided in Baumeister and Benati (2013) who report an annualizedimpact response of about 2% for 2010. Compared to findings on the conventionalmonetary policy shock, however, effects of the term spread shock are rather short-lived and peter out after one to two quarters. In Table 2 we present a forecast errorvariance decomposition. At the 20-quarters forecast horizon, the monetary policyshock explains about 20% to 30% more forecast error variance than the spread shock.

The mid-panel of Fig. 2 shows impulse responses of consumer price inflation.Both shocks drive up inflation by about 0.2 to 0.3 percentage points on impact,as we have ruled out a price puzzle by assumption. Adjustment of inflation turnsnegative in response to lowering the policy rate, while effects are positive and thenquickly converge to zero in response to the spread shock. The spread shock accountsfor a larger part of forecast error variance throughout the sample period.

Next we analyze the potential transmission mechanisms starting with the wealthchannel. In the bottom panel of Fig. 2 we depict responses for consumer wealthgrowth. Looking at the conventional monetary policy shock first, we find positiveresponses of consumer wealth throughout most of the sample period. These effects,however, are very short-lived and peter out immediately after impact. By contrast,the reduction of the term spread spurs wealth growth throughout the sample pe-riods and effects tend to be slightly more persistent compared to responses to themonetary policy shock discussed before. In terms of forecast error variance andwith the exception of the period of the global financial crisis, the term spread shockexplains about 1.5 to 2 times as much variance as the monetary policy shock. Takenat face value the results reveal the wealth channel as an important facet of thetransmission mechanism through which unconventional monetary policy can affectaggregate demand. In terms of persistence, the channel seems less important whenmonetary policy is conducted by steering short-term interest rates. This result is inline with Ludvigson et al. (2002) who attest the wealth channel only a minor rolein the transmission of conventional monetary policy to consumption.

Last, we investigate the bank lending / credit channel. Figure 3 shows theresponses of growth in assets and deposits of commercial banks as well as net interestrate margins and Table 2 the corresponding forecast error variance decomposition.

[Fig. 3 about here.]

The impact response of asset growth to a conventional monetary policy shockis shown in the top panel of the figure. A loosening of monetary policy spurs assetgrowth for all three time periods considered, responses are tightly estimated andthe effects tend to be very persistent. Next we look at growth of deposits depictedin the mid-panel of Fig. 3. Albeit for both shocks we have assumed an immediateacceleration of deposit growth, the effects of the term spread immediately peterout after one quarter, while responses to the conventional monetary policy shockare rather persistent and mostly tightly estimated. That is, the impact of theterm spread shock on asset and deposit growth is negligible, while we find tightlyestimated responses to the conventional monetary policy shock. This impressionis broadly confirmed by a forecast error variance decomposition, shown in Table 2.At the 20 quarter forecast horizon, the spread shock accounts for 11% of both,

11

error variance of banks’ asset and deposit growth. Shares of explained variance inbanks’ deposit growth are comparable to that explained by the spread shock. Sharesrelated to banks’ asset growth explained by the monetary policy shock are somewhathigher. Strong and persistent effects of a conventional monetary policy shock onasset and deposit growth and a large share of explained forecast error variance,reveal an important role for the credit / bank lending channel for monetary policytransmission. By contrast, this channel seems less important in case stimulus comesfrom lowering the term spread.

For completeness we show responses of net-interest rate margins in the bottompanel of Fig. 3. An unexpected decrease of the policy rate triggers an increase innet interest rate margins – probably driven by an imperfect pass through of thepolicy rate change to the long end of the yield curve. After four quarters, effectsstart hovering around zero and are accompanied by wide credible sets. Responses tothe term spread shock show a different pattern: Net interest rate margins decreasein response to a lowering of the term spread. These effects are very persistent forall three time periods considered. Naturally, and since net interest margins followthe term spread, the term spread shock explains considerably more forecast errorvariance as the conventional monetary policy shock. This holds true throughout thesample period

Summing up, we find that both shocks accelerate output growth and drive upinflation. While the effects of a conventional monetary policy shock on outputgrowth are rather persistent and tightly estimated, effects of the term spread shockare short-lived. Responses of CPI inflation are accompanied by wide credible sets forboth shocks. Positive effects on output growth seem to be driven by an expansionof asset and deposit growth of the banking sector lending empirical support forthe importance of the credit / bank lending channel in case stimulus comes fromlowering the policy rate. By contrast, the spread shock has no significant effecton asset and deposit growth. Rather, positive (and short-lived) effects on outputgrowth are triggered by an acceleration of consumer wealth growth.

3.2 Do effects vary over time?

Having established through which channels both shocks transmit to the real econ-omy, we now investigate more closely their overall effects. The strength of bothshocks might depend on the specific economic environment when the shock is car-ried out. For example, Jannsen et al. (2014) find strong effects of monetary policyduring recessions associated with financial crises which holds especially true for therecent global financial crisis. They attribute their finding to the particular effective-ness of the credit / bank lending channel in a recession as advocated in Bernankeand Gertler (1995). Others find the opposite, namely that monetary policy is lesseffective in times of heightened uncertainty (Bech et al., 2014; Aastveit et al., 2013).Considering the term spread shock, recent empirical research hints at diminishingeffectiveness of the LSAP programs (see, e.g., Wu, 2014).

So far, results reported in Figs. 2 and 3 have indicated changes in the strength ofthe shocks’ impacts on the variables considered in this study. However, these resultsmight be driven by the normalization of the shocks to 100 basis points, which isachieved by dividing through the impact response of the short-term interest rate and

12

the term spread (both which have diminished strongly since the period the FFR istechnically zero). To investigate this further, we report the ratio of the cumulativeresponse after 20 quarters to the one standard deviation shock on impact, with thestandard deviation varying over the sample. These ”elasticities” are thus free of thenormalization effect and show the responsiveness of a given variable in cumulativeterms to the two shocks on impact over the sample period.

[Fig. 4 about here.]

Elasticities shown in Fig. 4 reveal a very systematic pattern over time. Stimulusfrom conventional monetary policy is less effective during the period of the globalfinancial crisis compared to prior the crisis. This is particularly so in terms of out-put growth for which the elasticity reaches its trough over the whole sample periodduring the crisis. Hence we qualitatively corroborate findings of Bech et al. (2014),Aastveit et al. (2013), Hubrich and Tetlow (2014) who attribute smaller effects ofmonetary policy during financial crises to balance sheet adjustments and the delever-aging of the private sector on the one hand, and heightened uncertainty weighingon the business climate on the other hand. Strikingly, elasticities in the aftermathof the crisis do not simply revert back to their precrisis values. Responsiveness ofall variables except net interest rate margins even peaks during the aftermath of thecrisis. This finding is certainly less related to the episode of the crisis and its long-lasting consequences for the economy. Rather the specific monetary environmentwith the policy rate bound at zero seem to drive this result. Taken at face value,our finding implies that monetary policy is particularly effective if the policy rate isaltered after it has been committed to a particular value for a prolonged time.

Elasticities related to the term spread shock spike for most variables during thecrisis and during the period from 2000 to 2001. In the latter period, the Clintondebt buyback program took place, which was in many ways similar to an LSAP. SeeGreenwood and Vayanos (2010) for an in-depth analysis of the buyback programand its effect on the Treasury yield curve. This time pattern holds in particular truefor inflation, consumer wealth and growth in bank’s assets and deposits. The effectsof lowering the term spread on output growth have also diminished after the launchof the first LSAP. Our findings thus ascribe the latter LSAPs smaller effects onthe macroeconomy than the first programs, corroborating results of Wu (2014) andEngen et al. (2015). Engen et al. (2015) explicitly attribute the stronger effects of theearlier programs to the fact that they have been implemented at times when marketconditions were highly strained and a signal of commitment to accommodative policyover a longer horizon – such as the launch of quantitative easing – would be mosteffective.

Summing up, we find that monetary policy effectiveness in boosting aggregatedemand decreases significantly in the run-up of the global financial crisis. In theaftermath of the crisis, however, a hypothetical monetary policy shock would leadto strong effects on output growth and inflation. The opposite holds true for thespread shock, which is particularly stimulating during the period of the crisis whenthe Fed’s engagement in quantitative easing served as an important signal to longer-term accommodative monetary policy. In the aftermath of the crisis, effectivenessof the hypothetical spread shock declines.

13

3.3 Did term spread and monetary policy shocks matter historically?

Last, we examine the contribution of both shocks in explaining deviations fromtrend growth in the variables under consideration. These are depicted in Fig. 5.We would expect higher contributions of the monetary policy shock prior to theglobal financial crisis and increasing contributions of the spread shock thereafter.The historical decomposition of most time series actually corroborate this presump-tion. More specifically, monetary policy shocks explain larger shares of movementsin real GDP growth, inflation and banks’ asset growth prior and after the globalfinancial crisis. However, the ratio of monetary policy to spread shock contributiondeclined significantly from end-2008 to the end of our sample. Even more visible arecontributions related to the term spread, banks’ deposit growth, consumer wealthgrowth and net interest margins, for which the spread shock explains a considerablelarger part of movements than the monetary policy shock in the aftermath of thecrisis.

[Fig. 5 about here.]

Summing up, a historical decomposition analysis revealed that the monetary pol-icy shock can explain movements in real GDP growth and inflation to a comparablylarger extent than the spread shock throughout the sample period. By contrast,the spread shock explains movements in the term spread, consumer wealth growth,banks’ deposit growth and net interest margins to a comparably larger extent. Forall variables considered, the importance of the spread shock has increased signifi-cantly since end-2008, the period in which the first LSAP was launched. This findingis in line with our expectations and thus leads further confidence in the statisticalframework used in this study.

3.4 Robustness and extensions

In this section we investigate the robustness of our results. We do this by firstlooking at another measure of banks’ asset growth taking a broader definition of thebanking sector, by including investment growth as a further variable to the systemand last by imposing different orderings of the variables to demonstrate that ourestimates remain qualitatively unaffected.

First, since the shadow banking sector has expanded rapidly over the last decadein the USA, it has been argued that focusing on commercial banks’ assets mightyield an incomplete assessment of monetary policy transmission (see e.g., Adrianet al., 2010; Nelson et al., 2015). Hence we substitute commercial banks’ assetswith assets of the shadow banking sector and re-run the analysis outlined in section3. Shadow banks are defined as financial intermediaries that conduct functionsof banking without access to central bank liquidity and in the definition followingNelson et al. (2015) comprise finance companies, issuers of asset-backed securitiesand funding corporations.12 In a nutshell, credit intermediation through the shadow

12Data on shadow assets (FL504090005.Q, FL674090005.Q, FL614090005.Q) are from the finan-cial accounts data base of the federal reserve system, http://www.federalserver.gov/releases/z1/about.htm.

14

banking system is comparable to credit intermediation of a traditional bank withwholesale investors at the deposit end and at the loan origination end are financecompanies and traditional banks.

[Fig. 6 about here.]

Figure 6 shows impulse responses of asset growth, deposit growth and real GDPgrowth. Overall, results on real activity are nearly unaffected by inclusion of shadowassets, albeit uncertainty of the estimates is slightly more elevated especially inthe most recent part of our sample. While the shape of asset and deposit growthresponses is very similar to our baseline estimates, including shadow assets yieldsstronger responses in terms of overall magnitudes. This holds true for all timeperiods considered, for both shocks and for both variables. However, these strongermagnitudes are estimated with a lot of uncertainty and hence do not translate intooverall stronger responses of real GDP growth. Responses of the other variables arevery similar to results of our baseline estimation. This is also evident from Table 3,top panel, which lists correlations of median impulse responses with the baselinemodel. The fact that we get very similar results of asset responses to both shockscontrasts findings of Nelson et al. (2015) who report a decrease of commercial banks’assets and an increase of shadow assets in response to a contractionary monetarypolicy shock. Note that we have not restricted the responses of asset growth andour results are hence purely data driven. They might differ from those of Nelson etal. (2015) since we use a richer framework in terms of included variables and coveredtransmission channels.

Second, and as pointed out in Stein (2012), a reason why effects of asset purchaseprograms might have diminished over time are smaller effects via investment spend-ing. In principle, a decrease in longer-term borrowing costs for firms should boostinvestment spending. If, however, borrowing costs are further reduced by additionalasset purchase programs, firms might simply pay back short-term debt and issuemore and cheaper long term debt. In that case, there is no additional impetus tothe economy via investment spending. To investigate this in more depth, we re-runour analysis with gross fixed investment growth as an additional variable. We alsomodify the characterization of the two shocks provided by the restrictions in Table 1.Here we add further restrictions saying that investment growth ticks up in responseto both, a conventional monetary policy expansion and a shock to the term spread.Fig. 7 shows the elasticity of the cumulative response with respect to the initial sizeof the shock.

[Fig. 7 about here.]

Looking at investment growth points indeed to a smaller elasticity in the after-math of the crisis compared to the crisis period itself. The pattern of the othervariables is consistent with our baseline estimates, stronger effects during the crisisand smaller impacts in the aftermath regarding the term spread shock, while theopposite holds true for the monetary policy shock. In general, including investmentgrowth has rendered elasticities more volatile in the aftermath of the crisis. Thisis due to the fact that with the additional restrictions imposed it is harder to find

15

rotation matrices fulfilling the complete set of identifying assumptions. More specif-ically, while impulse responses of our baseline estimate are typically based on 250 to300 rotation matrices each ten quarters we sample them, the number of successfullysampled matrices decreases to about 150 per sampling point when including invest-ment growth. Considering impulse responses, not shown, the inclusion of investmentgrowth leaves our results broadly unchanged.

Last and to add further confidence to our results, we change the ordering ofthe variables for our estimation set up. For the baseline ordering we put real GDPgrowth first, followed by inflation, wealth, short-term interest rates, banks’ depositsand assets, the term spread and net interest rate margins. This ordering is motivatedin Christiano et al. (1996) and states that output cannot be contemporaneously af-fected by inflation, consumer wealth and the policy rate. Results of the baselineordering are compared to results under 10 randomly chosen orderings. As stressedbefore and since we rely on an explicit identification of the shocks via sign restric-tions, the ordering of the variables should not affect our results qualitatively. Thisis evident in the bottom panel of Table 3 which shows average correlations of me-dian impulse responses under the baseline and the 10 permuted orderings. In fact,correlations are in almost all cases virtually unity. These small differences can bewell attributed to sampling error.

[Table 3 about here.]

4 Conclusions

In this paper we have analyzed the effects and transmission of conventional andunconventional monetary policy in the USA. For that purpose we have proposeda medium- to large scale model that allows parameters to drift and residual vari-ances to change over time. Our main results remain qualitatively unaffected whenconsidering an alternative measure for banking sector assets, including investmentgrowth as a further transmission channel and using different Cholesky orderings inthe estimation stage of the model. These can be summarized as follows:

First, we find positive and rather persistent effects on output growth in responseto a conventional monetary policy shock. These effects seem to be driven by anexpansion of asset and deposit growth of the banking sector and thus by a broadcredit / bank lending channel. By contrast and in line with previous findings (seee.g., Ludvigson et al., 2002), the wealth channel appears less important for thetransmission of conventional monetary policy in the USA. A forecast error variancedecomposition lends further support to these findings.

Second, we find a pronounced and distinct pattern of monetary policy effective-ness over time. More specifically, our results point to comparably modest effects onoutput growth in response to a hypothetical and unexpected lowering of the policyrate during the period of the global financial crisis. In this sense, our results corrobo-rate findings of a recent strand of the literature stating that monetary policy is weakin recessions associated with either high economic uncertainty or more generally fi-nancial crises (see e.g., Aastveit et al., 2013; Bech et al., 2014; Hubrich and Tetlow,2014; Tenreyro and Thwaites, 2013). There is less empirical work on the effective-ness of monetary policy in the aftermath of the global financial crisis, a period in

16

which the main US policy rate was effectively zero. Our results show the strongestresponsiveness of the economy to a hypothetical monetary policy shock during thatperiod. From the perspective of a policymaker seems less relevant in practical terms,since obviously the policy rate cannot enter negative territory. However, it is ratherthe fact that the policy rate has not changed for an extended time than the level atwhich the policy rate stood that drives this result. If changes in the policy rate arerare, volatility associated to a monetary policy shock is low and a deviation fromthe commitment can provide a particularly strong boost to output growth. Note,however, that a central bank’s loss function typically consists of other additionaltargets such as price stabilization and hence our finding does not directly translateinto a policy recommendation to deviate from a commitment. Still, it suggest thateffects of a correction of the monetary policy stance after an extended period ofunchanged monetary policy might have large macroeconomic effects.

Third, and looking at the term spread shock, we find positive but short-livedeffects on output and consumer price growth. These work mainly through theconsumer wealth channel and via steering inflation, while there is less evidenceof impetus via banks’ asset and deposit growth. The decrease in the term spreadtriggers a fall in net interest rate margins, making it less attractive for banks to lend.Moreover, deposits will increase only shortly at a given bank since investors tendto reinvest their proceeds from selling long-term assets to the central bank ratherquickly (Butt et al., 2014). This leaves little room for the ”credit / bank lending”channel to operate in case stimulus comes from lowering the term spread.

Last, we find that the term spread shock impacts most strongly on output growthduring the period of the global financial crisis and less so in its aftermath. Takenat face value, this result implies that the effectiveness of the Fed’s unconventionalmonetary policy measures has abated since the early programs. Smaller effectsin the most recent period stem from a decrease in stimulus of consumer wealthand a smaller responsiveness of inflation. These might be attributed to an implicitsignaling channel which is particularly effective when financial markets are impairedand economic conditions are characterized by high uncertainty (Engen et al., 2015).In addition, we show that effects of quantitative easing on investment growth havediminished over time providing thereby less stimulus for overall GDP growth.

17

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Table

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ote

s:A

llre

stri

ctio

ns

imp

ose

don

imp

act

on

ly.

For

the

sake

of

com

ple

ten

ess

an

du

nre

late

dto

the

iden

tifi

cati

on

sch

eme,

note

that

we

set

all

coeffi

cien

tsin

the

inte

rest

rate

equ

ati

on

toze

rofo

rth

efi

rst

eight

qu

art

ers

ass

um

ing

that

the

inte

rest

rate

sd

on

ot

resp

on

dto

eith

ersh

ock

s.B

yth

isw

em

imic

an

exte

nd

edp

erio

dof

the

inte

rest

rate

tied

toth

eze

rolo

wer

bou

nd

(Bau

mei

ster

an

dB

enati

,2013).

22

Table

2:

For

ecas

ter

ror

vari

ance

dec

omp

osit

ion

Moneta

ryp

oli

cysh

ock

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

1991

Q1-

2015

Q1

Rea

lG

DP

grow

th0.

100.

100.

080.

10In

flat

ion

0.06

0.04

0.04

0.06

Con

sum

erw

ealt

hgr

owth

0.07

0.08

0.07

0.07

Shor

t-te

rmin

tere

stra

te0.

070.

070.

060.

07B

anks’

dep

osit

grow

th0.

100.

100.

090.

10B

anks’

asse

tgr

owth

0.13

0.11

0.10

0.12

Ter

msp

read

0.11

0.13

0.10

0.11

Net

inte

rest

rate

mar

gin

0.08

0.09

0.07

0.08

Term

spre

ad

shock

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

1991

Q1-

2015

Q1

Rea

lG

DP

grow

th0.

080.

070.

070.

08In

flat

ion

0.10

0.05

0.08

0.09

Con

sum

erw

ealt

hgr

owth

0.13

0.08

0.10

0.12

Shor

t-te

rmin

tere

stra

te0.

120.

060.

090.

11B

anks’

dep

osit

s0.

110.

080.

100.

11B

anks’

asse

ts0.

110.

090.

100.

11T

erm

spre

ad0.

120.

060.

090.

11N

etin

tere

stra

tem

argi

n0.

170.

100.

120.

15

Th

eta

ble

show

sa

fore

cast

erro

rva

rian

ced

ecom

posi

tion

aft

er20

qu

art

ers

base

don

the

post

erio

r;si

mple

aver

ages

over

the

tim

ep

erio

ds

con

sid

ered

.

23

Table

3:

Cor

rela

tion

ofm

edia

nim

puls

ere

spon

ses

Cor

rela

tion

ofsh

adow

asse

tsw

ith

bas

elin

e

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

Rea

lG

DP

grow

th0.

976

0.96

90.

968

0.98

40.

979

0.94

8In

flat

ion

0.95

70.

991

0.91

40.

963

0.95

00.

938

Wea

lth

0.99

60.

998

0.99

70.

995

0.99

60.

994

Shor

t-te

rmin

tere

stra

te0.

994

0.99

90.

978

1.00

00.

999

1.00

0B

anks’

dep

osit

s0.

775

0.65

80.

768

0.93

00.

694

0.81

0B

anks’

asse

ts0.

934

0.95

60.

926

0.49

70.

457

0.62

5T

erm

spre

ad0.

999

0.99

90.

996

0.99

00.

978

0.98

8N

etin

tere

stra

tem

argi

n0.

996

0.99

40.

994

0.93

00.

694

0.78

6

Ave

rage

corr

elat

ion

ofdiff

eren

tC

hol

esky

order

ings

wit

hbas

elin

e

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

1991

Q1-

2007

Q3

2007

Q4-

2009

Q2

2009

Q3-

2015

Q1

Rea

lG

DP

grow

th0.

999

0.99

50.

999

0.99

80.

997

0.99

0In

flat

ion

0.99

80.

998

0.99

50.

996

0.99

80.

995

Wea

lth

0.99

90.

999

0.99

91.

000

1.00

00.

999

Shor

t-te

rmin

tere

stra

te0.

999

0.98

91.

000

1.00

01.

000

1.00

0B

anks’

dep

osit

s0.

998

0.99

30.

998

0.99

80.

984

0.98

6B

anks’

asse

ts0.

998

0.99

70.

999

0.99

90.

997

0.96

1T

erm

spre

ad1.

000

0.99

61.

000

0.99

90.

997

1.00

0N

etin

tere

stra

tem

argi

n1.

000

0.99

80.

999

0.98

70.

954

0.83

7

Note

s:T

he

tab

lesh

ow

sco

rrel

ati

on

of

med

ian

imp

uls

ere

spon

ses

toth

eco

nven

tion

al

an

du

nco

nven

tion

al

mon

etary

policy

shock

s,over

thre

ese

lect

edh

ori

zon

s.C

orr

elati

on

sin

the

top

pan

elre

fer

toth

ose

of

am

od

elu

sin

gass

ets

of

the

shad

ow

ban

kin

gse

ctor

inst

ead

of

com

mer

cial

ban

ks’

ass

ets

wit

hes

tim

ate

sof

the

base

lin

em

od

el.

Corr

elati

on

sin

the

bott

om

pan

elre

fer

toes

tim

ate

su

sin

g10

ran

dom

lyp

erm

ute

dC

hole

sky

ord

erin

gs

an

dth

eb

ase

lin

em

od

el.

24

Fig

.1:

Ter

msp

read

and

mon

etar

yp

olic

ysh

ock

(a)

Ter

msp

read

shock

−1.5−1.0−0.50.00.5

Q1 1992

Q2 1993

Q3 1994

Q1 1996

Q2 1997

Q4 1998

Q1 2000

Q2 2001

Q4 2002

Q1 2004

Q3 2005

Q4 2006

Q1 2008

Q3 2009

Q4 2010

Q2 2012

Q3 2013

Q1 2015

(b)

Mon

etar

yp

olic

ysh

ock

−0.6−0.4−0.20.00.20.4

Q1 1992

Q2 1993

Q3 1994

Q1 1996

Q2 1997

Q4 1998

Q1 2000

Q2 2001

Q4 2002

Q1 2004

Q3 2005

Q4 2006

Q1 2008

Q3 2009

Q4 2010

Q2 2012

Q3 2013

Q1 2015

Rom

er a

nd R

omer

(20

04)

mon

etar

y po

licy

shoc

k

(c)

Ter

msp

read

and

Fed

eral

Fu

nd

sR

ate

0246

Q1 1992

Q2 1993

Q3 1994

Q1 1996

Q2 1997

Q4 1998

Q1 2000

Q2 2001

Q4 2002

Q1 2004

Q3 2005

Q4 2006

Q1 2008

Q3 2009

Q4 2010

Q2 2012

Q3 2013

Q1 2015

Fed

eral

Fun

ds R

ate

Term

spr

ead

Notes:

Th

ep

lot

inth

ele

ftp

an

elsh

ow

sth

eid

enti

fied

term

spre

ad

shock

.V

erti

cal

bars

refe

rto

the

lau

nch

of

the

Cli

nto

nd

ebt

bu

yb

ack

pro

gra

man

dan

dth

eth

ree

LS

AP

pro

gra

ms.

Th

em

idp

an

elsh

ow

sth

em

on

etary

policy

shock

alo

ng

wit

hth

en

arr

ati

ve

mon

etary

policy

shock

of

Rom

eran

dR

om

er(2

004).

Th

eri

ght

pan

elsh

ow

sth

eev

olu

tion

of

the

term

spre

ad

an

dth

eF

eder

al

Fu

nd

sR

ate

(rea

lize

dd

ata

).

25

Fig

.2:

Impuls

ere

spon

sefu

nct

ions

Real

GD

Pgro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

2007

Q4−

2009

Q2

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

2009

Q3−

2015

Q1

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

1991

Q1−

2007

Q3

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

2007

Q4−

2009

Q2

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

2009

Q3−

2015

Q1

−0.

5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

02

46

810

1214

Inflati

on

Con

venti

onal

Unco

nve

nti

onal

1991

Q1−

2007

Q3

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

2007

Q4−

2009

Q2

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

2009

Q3−

2015

Q1

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

1991

Q1−

2007

Q3

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

2007

Q4−

2009

Q2

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

2009

Q3−

2015

Q1

−0.

6

−0.

3

0.0

0.3

0.7

1.0

1.3

1.6

02

46

810

1214

Consu

mer

wealt

hgro

wth

Con

venti

onal

Unco

nve

nti

onal

1991

Q1−

2007

Q3

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

2007

Q4−

2009

Q2

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

2009

Q3−

2015

Q1

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

1991

Q1−

2007

Q3

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

2007

Q4−

2009

Q2

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

2009

Q3−

2015

Q1

−5.

0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

02

46

810

1214

Note

s:P

ost

erio

rm

edia

nre

spon

ses

toan

exp

an

sion

ary

mon

etary

policy

shock

(-100b

p)

an

dsh

ock

toth

ete

rmsp

read

(-100b

p)

alo

ng

wit

h50%

(dark

blu

e)an

d68%

(lig

ht

blu

e)cr

edib

leb

ou

nd

s.R

esu

lts

show

nas

aver

ages

over

thre

ep

erio

ds,

pre

-cri

sis

from

1991Q

1to

2007Q

3,

glo

bal

fin

an

cial

cris

isfr

om

2007Q

4to

2009Q

2an

dit

saft

erm

ath

from

2009Q

3to

2015Q

1.

26

Fig

.3:

Impuls

ere

spon

sefu

nct

ions

Banks’

ass

et

gro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

2007

Q4−

2009

Q2

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

2009

Q3−

2015

Q1

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

1991

Q1−

2007

Q3

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

2007

Q4−

2009

Q2

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

2009

Q3−

2015

Q1

−15

.0

−10

.0

−5.

0

0.0

5.0

10.

0

15.

0

20.

0

02

46

810

1214

Banks’

dep

osi

tgro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

2007

Q4−

2009

Q2

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

2009

Q3−

2015

Q1

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

1991

Q1−

2007

Q3

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

2007

Q4−

2009

Q2

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

2009

Q3−

2015

Q1

−8.

0

−4.

9

−1.

7

1.4

4.6

7.7

10.9

14.0

02

46

810

1214

Net

inte

rest

marg

ins

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

2007

Q4−

2009

Q2

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

2009

Q3−

2015

Q1

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

1991

Q1−

2007

Q3

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

2007

Q4−

2009

Q2

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

2009

Q3−

2015

Q1

−1.

5

−1.

0

−0.

5

0.0

0.5

1.0

1.5

2.0

02

46

810

1214

Note

s:P

ost

erio

rm

edia

nre

spon

ses

toan

exp

an

sion

ary

mon

etary

policy

shock

(-100b

p)

an

dsh

ock

toth

ete

rmsp

read

(-100b

p)

alo

ng

wit

h50%

(dark

blu

e)an

d68%

(lig

ht

blu

e)cr

edib

leb

ou

nd

s.R

esu

lts

show

nas

aver

ages

over

thre

ep

erio

ds,

pre

-cri

sis

from

1991Q

1to

2007Q

3,

glo

bal

fin

an

cial

cris

isfr

om

2007Q

4to

2009Q

2an

dit

saft

erm

ath

from

2009Q

3to

2015Q

1.

27

Fig. 4: Elasticity of cumulative response to size of shock on impact

(a) Real GDP growth

0.0

0.7

1.4

2.1

2.8

3.5

4.2

4.9

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(b) Inflation

0.0

0.2

0.4

0.7

0.9

1.1

1.3

1.6

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(c) Consumer wealth growth

0.0

3.7

7.3

11.0

14.6

18.3

22.0

25.6

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(d) Banks’ asset growth

0.0

5.2

10.4

15.6

20.8

25.9

31.1

36.3

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(e) Banks’ deposit growth

0.0

4.0

8.0

12.0

16.0

20.0

23.9

27.9

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(f) Net interest rate margin

0.0

1.1

2.1

3.2

4.2

5.3

6.3

7.4

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

Notes: The figure shows the ratio of the cumulative response of particular variable to the impact shock of theconventional monetary policy shock (black, solid line) and the spread shock (red, dashed line). Elasticities arein absolute terms. The shaded grey area indicates the period of the recession associated with the global financialcrisis. 28

Fig. 5: Historical decomposition of time series

(a) Real GDP growth−

0.01

5−

0.01

0−

0.00

50.

000

0.00

50.

010

−0.

015

−0.

010

−0.

005

0.00

00.

005

0.01

0

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(b) Inflation

−0.

015

−0.

010

−0.

005

0.00

00.

005

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

015

−0.

010

−0.

005

0.00

00.

005

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(c) Short-term interest rates

−0.

06−

0.04

−0.

020.

000.

020.

04

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

06−

0.04

−0.

020.

000.

020.

04

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(d) Term spread

−0.

020.

000.

020.

040.

060.

08

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

020.

000.

020.

040.

060.

08

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(e) Consumer wealth growth

−0.

08−

0.06

−0.

04−

0.02

0.00

0.02

0.04

0.06

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

08−

0.06

−0.

04−

0.02

0.00

0.02

0.04

0.06

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(f) Banks’ asset growth

−0.

04−

0.02

0.00

0.02

0.04

0.06

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

04−

0.02

0.00

0.02

0.04

0.06

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(g) Banks’ deposit growth

−0.

10−

0.08

−0.

06−

0.04

−0.

020.

000.

020.

04

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

−0.

10−

0.08

−0.

06−

0.04

−0.

020.

000.

020.

04

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

(h) Net interest rate margin

0.00

0.01

0.02

0.03

0.04

0.05

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

0.00

0.01

0.02

0.03

0.04

0.05

1992

−01

−01

1993

−01

−01

1994

−04

−01

1995

−07

−01

1996

−10

−01

1998

−01

−01

1999

−04

−01

2000

−04

−01

2001

−07

−01

2002

−10

−01

2004

−01

−01

2005

−04

−01

2006

−07

−01

2007

−07

−01

2008

−10

−01

2010

−01

−01

2011

−04

−01

2012

−07

−01

2013

−10

−01

2015

−01

−01

Notes: Historical decomposition of time series based on the posterior median. The overall contribution of all shocksexcept the term spread and monetary policy shock in red. Contributions of the monetary policy shock and the termspread shock in blue and yellow, respectively. The shaded grey area indicates the period of the recession associatedwith the global financial crisis.

29

Fig

.6:

Impuls

ere

spon

ses

wit

hsh

adow

asse

ts

Real

GD

Pgro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

2007

Q4−

2009

Q2

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

2009

Q3−

2015

Q1

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

1991

Q1−

2007

Q3

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

2007

Q4−

2009

Q2

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

2009

Q3−

2015

Q1

−0.

5

0.1

0.6

1.2

1.8

2.4

2.9

3.5

02

46

810

1214

Banks’

ass

et

gro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

2007

Q4−

2009

Q2

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

2009

Q3−

2015

Q1

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

1991

Q1−

2007

Q3

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

2007

Q4−

2009

Q2

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

2009

Q3−

2015

Q1

−15

.0

−7.

1

0.7

8.6

16.

4

24.

3

32.

1

40.

0

02

46

810

1214

Banks’

dep

osi

tgro

wth

Mon

etar

yp

olic

ysh

ock

Ter

msp

read

shock

1991

Q1−

2007

Q3

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

2007

Q4−

2009

Q2

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

2009

Q3−

2015

Q1

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

1991

Q1−

2007

Q3

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

2007

Q4−

2009

Q2

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

2009

Q3−

2015

Q1

−4.

0

−1.

4

1.1

3.7

6.3

8.9

11.4

14.0

02

46

810

1214

Note

s:P

ost

erio

rm

edia

nre

spon

ses

toan

exp

an

sion

ary

mon

etary

policy

shock

(-100b

p)

an

dsh

ock

toth

ete

rmsp

read

(-100b

p)

alo

ng

wit

h50%

(dark

blu

e)an

d68%

(lig

ht

blu

e)cr

edib

leb

ou

nd

s.R

esu

lts

show

nas

aver

ages

over

thre

ep

erio

ds,

pre

-cri

sis

from

1991Q

1to

2007Q

3,

glo

bal

fin

an

cial

cris

isfr

om

2007Q

4to

2009Q

2an

dit

saft

erm

ath

from

2009Q

3to

2015Q

1.

Res

ult

sb

ase

don

incl

usi

on

of

ass

ets

of

the

shad

ow

ban

kin

gse

ctor

inst

ead

of

com

mer

cial

ban

ks’

ass

ets.

30

Fig. 7: Elasticity of cumulative response to size of shock on impact - investmentgrowth included

(a) Real GDP growth

0.0

0.5

1.0

1.5

2.0

2.4

2.9

3.4

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(b) Inflation

0.0

0.20.3

0.5

0.70.8

1.01.1

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(c) Consumer wealth growth

0.0

3.5

6.9

10.4

13.8

17.3

20.8

24.2

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(d) Investment growth

0.0

3.9

7.8

11.7

15.6

19.5

23.4

27.3

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(e) Banks’ asset growth

0.0

3.3

6.6

9.9

13.2

16.5

19.8

23.1

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

(f) Banks’ deposit growth

0.0

2.6

5.2

7.7

10.3

12.9

15.5

18.1

Q1

1992

Q4

1993

Q3

1995

Q2

1997

Q1

1999

Q4

2000

Q3

2002

Q2

2004

Q1

2006

Q4

2007

Q2

2009

Q3

2010

Q1

2012

Q3

2013

Q1

2015

Notes: The figure shows the ratio of the cumulative response of particular variable to the impact shock of theconventional monetary policy shock (black, solid line) and the spread shock (red, dashed line). Elasticities arein absolute terms. The shaded grey area indicates the period of the recession associated with the global financialcrisis.

31

Appendix A Structural identification

To implement the sign restrictions technically, note that Eq. (2.8) can be written as

Atyt = ct +

p∑j=1

Bjtyt−j + Λ0.5t vt, (A.1)

where Λ = Λ0.5t Λ0.5

t and vt ∼ N (0, Im) is a standard normal vector error term.Multiplication from the left by Λ−0.5

t yields

Atyt = ct +

p∑j=1

Bjtyt−j + vt (A.2)

with At = Λ−0.5t At, ct = Λ−0.5

t ct and Bjt = Λ−0.5t Bjt.

It can be shown that left multiplying Eq. (A.2) with an m×m-dimensional or-thonormal matrix R with R′R = Im leaves the likelihood function untouched. Thisimplies that impulse responses are set-identified. To implement the sign restrictionsapproach we simply draw R using the algorithm outlined in Rubio-Ramırez et al.(2010) until the impulse response functions satisfy a given set of sign restrictions tobe chosen by the researcher. This has to be done for each draw from the posterior,which in our application boils down to 500 draws randomly taken from the full set of15,000 posterior draws. To speed up computation we do not search for each point intime a new rotation matrix. Instead we look for new rotation matrices after 10 quar-ters and check whether the restrictions are fulfilled throughout the sample. Theseleaves us with 11 time periods for which we look for new rotation matrices. Foreach of these time points we recovered 250 to 300 rotation matrices that fulfilled ourrestrictions. There was no visible time pattern over the amount of sign restrictionsrecovered throughout our sample period.

To impose the additional restriction that the short-term interest rate reacts slug-gishly with respect to an unconventional monetary policy shock, we construct thefollowing deterministic rotation matrix (Baumeister and Benati, 2013)

S =

(Im−2 0m−2×2

02×m−2 U

)(A.3)

with

U =

(cos(ϑ) − sin(ϑ)sin(ϑ) cos(ϑ)

). (A.4)

The rotation angle is defined as

ϑ = tan−1([AtR′]ij/[AtR

′]ii). (A.5)

Here, the notation [AtR′]ij selects the i, jth element of the impact matrix, corre-

sponding to the contemporaneous response of variable the short-term interest rate(variable i) to an unconventional monetary policy shock (variable j). Multiplyingthe impact matrix with U from the right yields a new impact matrix that satisfies

32

the set of sign restrictions specified in subsection 2.4 and the zero impact restrictiondescribed above.

Since we assume that the central bank is constrained by the zero lower bound,we zero-out the structural coefficients of the monetary policy rule for the first eightquarters after the shock hit the economy. This procedure, however, is subject tothe Lucas critique because economic agents are not allowed to change their behavioraccordingly. However, the findings in Baumeister and Benati (2013) suggest that thedifferences between the results obtained by manipulating the structural coefficientsor by manipulating the historical structural shocks to keep the interest rate at thezero lower bound are quite similar. Moreover, manipulating the structural shocksgives rise to additional shortcomings like the fact that this approach ignores theimpact of agents expectations about future changes in the policy rate. In addition,the systematic component of monetary policy implies that the short-term interestrate reacts to different shocks. However, the unsystematic part, by construction,offsets this behavior and the corresponding shocks would no longer originate from awhite noise process.

Appendix B A brief sketch of the Markov chain Monte Carlo algorithm

Since we impose a Cholesky structure on the model a-priori and estimate the systemequation-by-equation our Markov chain Monte Carlo (MCMC) algorithm consistsof the following three steps:

1. Sample aT = (a1, . . . ,aT )′ and bT = (b1, . . . , bT )′ using the Carter and Kohn(1994) algorithm.

2. Sample the variances of Eqs. (2.5) and (2.6) using Gibbs steps by noting thatthe conditional posteriors are again of inverted Wishart form.

3. Sample hT = (h1, . . . , hT )′ and the corresponding parameters of Eq. (2.7)through the algorithm put forth in Kastner and Fruhwirth-Schnatter (2013).A brief description of this algorithm is provided in Appendix C.

Step 1 is a standard application of Gibbs sampling in state-space models. In step2 we draw the parameters of the corresponding state equations conditional on thestates. Step 3 is described in more detail in the appendix. Finally note that wesample the parameters of the different equations simultaneously.

Appendix C Sampling log-volatilities

To simulate the full history of log-volatilities for the ith equation hTi = (hi1, . . . , hiT )′

we use the algorithm outlined in Kastner and Fruhwirth-Schnatter (2013). Thisalgorithm samples hTi all without a loop. This is achieved by rewriting hTi in terms ofa multivariate normal distribution. Moreover the parameters of the state equation inEq. (2.7) are sampled through simple Metropolis Hastings (MH) or Gibbs samplingsteps. To achieve a higher degree of sampling efficiency we sample the corresponding

33

parameters from the centered parameterization in Eq. (2.7) and a non-centeredvariant given by

hit = ρihit−1 + εit, εit ∼ N (0, 1). (C.1)

To simplify the exposition we illustrate the algorithm for the case when i = 2, . . . ,m.For i = 1 the same steps apply with only minor modifications. Let us begin byrewriting Eq. (2.4) as

eit = cit −i−1∑s=1

ais,tyst −p∑j=1

bij,tyt−j = λ0.5it ε. (C.2)

Squaring and taking logarithms yields

e2it = hit + ln(u2

it). (C.3)

Since ln(u2it) follows a χ2(1) distribution we use a mixture of Gaussian distribution

to render Eq. (C.3) conditionally Gaussian,

ln(u2it)|rit ∼ N (mit, s

2it), (C.4)

where rit is a indicator controlling the mixture component to use at time t withrit ∈ 1, . . . , 10. mit and s2

it define the mean and the variance of the mixturecomponents employed.

The mixture indicators allow us to rewrite Eq. (C.3) as a linear Gaussian statespace model

e2it = mir,t + hit + ξit, ξit ∼ N (0, s2

ir,t). (C.5)

The algorithm then consists of the following steps.

1. Sample hi,−1|rit, µi, ρi, σih,Ψit or hij,−1|rij, ρi, σih,Ψit all without a loop (AWOL).Here, Ψit = (cit, ais,t, . . . , aii−1,t, bi1,t, . . . , bip,t)

′ is a vector of stacked coeffi-cients and hi,−1 = (hi2, . . . , hiT )′. Following Rue (2001) hi,−1 can be writtenin terms of a multivariate normal distribution

hi,−1 ∼ N (Ω−1hici,Ω

−1hi

). (C.6)

Similarly the normal distribution corresponding to the non-centered parame-terization is given by

hi,−1 ∼ N (Ω−1

hici, Ω

−1

hi). (C.7)

The corresponding posterior moments are

Ωhi =

1s2rij,2

+ 1σ2ih

−ρiσ2ih

0 · · · 0

− ρiσ2ih

1s2ri,3

+ 1+ρiς2i

− ρiσ2ih

. . ....

0 − ρiσ2ih

. . . . . . 0...

. . . . . . 1s2rij,T−1

+ 1+ρiσ2ih

−ξijσ2ih

0 . . . 0 − ρiσ2ih

1s2rij,T

+ 1σ2ih

(C.8)

34

and

ci =

1

s2rij,2(y2ij,2 −mrij,2) + µi(1−ρi)

σ2ih

...1

s2rij,T(y2ij,T −mrij,T ) + µi(1−ρi)

σ2ih

. (C.9)

Multiplying by σ2ih yields the moments for the non-centered parameterization:

Ωi = σ2ihΩhij and cij = σ2

ihcij. Finally, the initial states of hTi , hi1 and hi1 areobtained from their respective stationary distributions.

2. Obtain the parameters of Eq. (2.7) and Eq. (C.3). Since we impose a non-conjugate Gamma prior on σih we employ a Metropolis-within-Gibbs algorithmto sample µi, ρi and σi for both parameterizations. For the centered variantwe simulate µi and ρi with a single Gibbs step and σ2

i is sampled through aMH step. For the non-centered parameterization, we sample ρi with MH andthe other parameters with Gibbs steps.

3. Sample the mixture indicators with inverse transform sampling. Note that wecan rewrite Eq. (C.3) as

e2it − hit = ξit, ξit ∼ N (mir,t, s

2it). (C.10)

This allows us to compute the posterior probabilities that rit = j, which aregiven by

p(rit = c|•) ∝ p(rit = c)1

sikexp

(−(ξit −mik)

2s2rit

), (C.11)

where p(rit = c|•) are the unnormalized weights associated with the cth mix-ture component.

The algorithm simply draws the parameters under both parametrizations and de-cides ex-post which of the parametrizations to use. This choice depends on therelationship between the variances of Eq. (2.7) and Eq. (C.3). For more informationsee Kastner and Fruhwirth-Schnatter (2013) and Kastner (2013).

The sampled log-volatilities are shown in Fig. C.1.Reduced form volatility of the short-term interest rate and the term spread has

increased considerably in the run-up of the global financial crisis – a period duringwhich the Fed has aggressively lowered interest rates. Volatility has spiked aroundmid-2008 and hence in the midst of the crisis. While the crisis peak of residualvariance associated to short-term interest rate marked also the peak over our sampleperiod, volatility of the term spread peaked in the early 1990s.

The mid panel of Fig. C.1 shows the volatilities for variables related to the realside of the economy. Residual variance associated to real GDP growth was ele-vated in the early 2000s and peaked around the same time as the financial variablesdiscussed above. During the early 2000s the so-called ”dot-com bubble” has burstcausing slowing down the US economy. Stochastic volatility of of wealth, whichis strongly anchored on movements in stock market prices, naturally was also ele-vated during that period. In contrast to volatility of real GDP, residual variance of

35

wealth was pronounced for a longer period during the global financial crisis. Resid-ual variance of CPI inflation started to rise more considerably from the beginningof the 2000s until 2008 – a period which was characterized by sound growth in pricedynamics in the USA. Residual variance peaked in the aftermath of the crisis andhence a little later than that associated to real GDP growth, when CPI inflationreverted from negative to positive territory.

Last, the bottom panel of Fig. C.1 shows residual variance for variables relatedto the banking sector. Residual variance of asset growth of commercial banks waselevated during the early 2000s and the global financial crisis, where it peaked aroundthe same time as residual variance of real GDP growth, short-term interest rates andthe term spread. Since 2009 estimated volatility has declined and is considerablysmaller at the most recent period in our sample compared to its peak value. Residualvariance associated with bank deposits and net interest margins show a slightlydifferent pattern. Bank deposit volatility increased gradually from the beginningof 2004 until 2009, after which it gradually started to decline until the end of oursample period. Volatility associated to net interest margins spiked around 1997 andpeaked in late 2009. That is, for both variables, banking deposits and net interestmargins, volatility spikes during the global financial crisis occurred slightly laterthan that of the other variables considered in this study.

36

Fig

.C

.1:

Sto

chas

tic

Vol

atilit

yov

erT

ime

(a)

Fin

an

cial

Var

iab

les

−10123

Q1 1992

Q2 1995

Q3 1998

Q4 2001

Q1 2005

Q2 2008

Q3 2011

Q1 2015

S

hort

−te

rm in

tere

st r

ate

Term

spr

ead

(b)

Rea

lV

aria

ble

s

−101234

Q1 1992

Q2 1995

Q3 1998

Q4 2001

Q1 2005

Q2 2008

Q3 2011

Q1 2015

R

eal G

DP

CP

I Inf

latio

nW

ealth

(c)

Ban

kin

gV

aria

ble

s

−2−10123

Q1 1992

Q2 1995

Q3 1998

Q4 2001

Q1 2005

Q2 2008

Q3 2011

Q1 2015

B

anks

ass

ets

Ban

ks d

epos

itsN

et in

t. m

argi

n

Notes:

Pos

teri

orm

ean

ofre

sid

ual

vari

ance

over

tim

e.

37

Index of Working Papers: March 29, 2010

Markus Knell

161 Nominal and Real Wage Rigidities. In Theory and in Europe

May 31, 2010 Zeno Enders Philip Jung Gernot J. Müller

162 Has the Euro changed the Business Cycle?

August 25, 2010

Marianna Cervená Martin Schneider

163 Short-term forecasting GDP with a DSGE model augmented by monthly indicators

September 8, 2010

Sylvia Kaufmann Johann Scharler

164 Bank-Lending Standards, the Cost Channel and Inflation Dynamics

September 15, 2010

Helmut Elsinger

165 Independence Tests based on Symbolic Dynamics

December 14, 2010

Claudia Kwapil

166 Firms' Reactions to the Crisis and their Consequences for the Labour Market. Results of a Company Survey conducted in Austria

May 10, 2011 Helmut Stix

167 Does the Broad Public Want to Consolidate Public Debt? – The Role of Fairness and of Policy Credibility

May 11, 2011 Burkhard Raunig, Johann Scharler

168 Stock Market Volatility, Consumption and Investment; An Evaluation of the Uncertainty Hypothesis Using Post-War U.S. Data

May 23, 2011 Steffen Osterloh 169 Can Regional Transfers Buy Public Support? Evidence from EU Structural Policy

May 23, 2011 Friederike Niepmann Tim Schmidt-Eisenlohr

170 Bank Bailouts, International Linkages and Cooperation

September 1, 2011

Jarko Fidrmuc, Mariya Hake, Helmut Stix

171 Households’ Foreign Currency Borrowing in Central and Eastern Europe

September 9, 2011

Jürgen Eichberger, Klaus Rheinberger, Martin Summer

172 Credit Risk in General Equilibrium

October 6, 2011

Peter Lindner 173 Decomposition of Wealth and Income using Micro Data from Austria

October 18, 2011

Stefan Kerbl 174 Regulatory Medicine Against Financial Market Instability: What Helps And What Hurts?

December 31, 2011

Konstantins Benkovskis Julia Wörz

175 How Does Quality Impact on Import Prices?

January 17, 2012

Nicolás Albacete 176 Multiple Imputation in the Austrian Household Survey on Housing Wealth

January 27, 2012

Gerhard Fenz, Lukas Reiss, Martin Schneider

177 A structural interpretation of the impact of the great recession on the Austrian economy using an estimated DSGE model

July 27, 2012

Helmut Stix

178 Why Do People Save in Cash? Distrust, Memories of Banking Crises, Weak Institutions and Dollarization

August 20, 2012

Markus Knell

179 Increasing Life Expectancy and Pay-As-You-Go Pension Systems

September 25, 2012

Fabio Rumler, Walter Waschiczek

180 Have Changes in the Financial Structure Affected Bank Protability? Evidence for Austria

November 9, 2012

Elisabeth Beckmann, Jarko Fidrmuc, Helmut Stix

181 Foreign Currency Loans and Loan Arrears of Households in Central and Eastern Europe

June 10, 2013

Luca Fornaro

182 International Debt Deleveraging

June 10, 2013

Jenny Simon, Justin Valasek

183 Efficient Fiscal Spending by Supranational Unions

July 24, 2013

Thomas Breuer, Hans-Joachim Vollbrecht, Martin Summer

184 Endogenous Leverage and Asset Pricing in Double Auctions

September 23, 2013

Martin Feldkircher 185 A Global Macro Model for Emerging Europe

September 25, 2013

Martin Gächter, Aleksandra Riedl

186 One Money, One Cycle? The EMU Experience

December 9, 2013

Stefan Niemann, Paul Pichler

187 Collateral, Liquidity and Debt Sustainability

March 6, 2014

Elisabeth Beckmann, Helmut Stix

188 Foreign currency borrowing and knowledge about exchange rate risk

March 10, 2014

Jesús Crespo Cuaresma, Martin Feldkircher, Florian Huber

189 Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors

May 12, 2014

Claudia Steinwender 190 Information Frictions and the Law of One Price: “When the States and the Kingdom became United”

May 12, 2014

Saleem A. Bahaj 191 Systemic Sovereign Risk: Macroeconomic Implications in the Euro Area

May 16, 2014

John Bagnall, David Bounie, Kim P. Huynh, Anneke Kosse, Tobias Schmidt, Scott Schuh and Helmut Stix

192 Consumer Cash Usage: A Cross-Country Comparison with Payment Diary Survey Data

May 19, 2014

Konstantins Benkovskis Julia Wörz

193 “Made in China” - How Does it Affect Measures of Competitiveness?

June 25, 2014

Burkhard Raunig, Johann Scharler and Friedrich Sindermann

194 Do Banks Lend Less in Uncertain Times?

July 28, 2014

Martin Feldkircher and Florian Huber

195 The International Transmission of U.S. Structural Shocks – Evidence from Global Vector Autoregressions

September 16, 2014

Kim P. Huynh, Philipp Schmidt-Dengler, Helmut Stix

196 The Role of Card Acceptance in the Transaction; Demand for Money

October 10, 2014

Martin Brown, Helmut Stix

197 The Euroization of Bank Deposits in Eastern Europe

October 17, 2014

Ludmila Fadejeva, Martin Feldkircher, Thomas Reininger

198 Spillovers from Euro Area and U.S. Credit and Demand Shocks: Comparing Emerging Europe on the Basis of a GVAR Model

December 18, 2014

Esther Segalla

199 Shock Transmission through International Banks: Austria

March 5, 2015

Jonas Dovern, Martin Feldkircher, Florian Huber

200 Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR

May 19, 2015

Markus Knell

201 The Return on Social Security with Increasing Longevity

June 15, 2015

Anil Ari

202 Sovereign Risk and Bank Risk-Taking

June 15, 2015

Matteo Crosignani

203 Why Are Banks Not Recapitalized During Crises?

February 19, 2016

Burkhard Raunig

204 Background Indicators

February 22, 2016

Jesús Crespo Cuaresma, Gernot Doppelhofer, Martin Feldkircher, Florian Huber

205 US Monetary Policy in a Globalized World

March 4, 2016

Helmut Elsinger, Philipp Schmidt-Dengler, Christine Zulehner

206 Competition in Treasury Auctions

May 14, 2016

Apostolos Thomadakis

207 Determinants of Credit Constrained Firms: Evidence from Central and Eastern Europe Region

July 1, 2016

Martin Feldkircher, Florian Huber

208 Unconventional US Monetary Policy: New Tools Same Channels?

Call for Entries: Visiting Research Program

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Applications (in English) should include

− a curriculum vitae,

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