dissertation paper student: angela-monica mĂrgĂrit supervisor: professor moisĂ altĂr july 2003...
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MEASURING CORE INFLATION IN MEASURING CORE INFLATION IN ROMANIAROMANIA
Dissertation Paper
Student: ANGELA-MONICA MĂRGĂRITSupervisor: Professor MOISĂ ALTĂR
July 2003
ACADEMY OF ECONOMIC STUDIES BUCHARESTDOCTORAL SCHOOL OF FINANCE AND BANKING
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I. INTRODUCTION II. THEORETICAL BACKGROUND
III. DATA AND ECONOMETRIC ESTIMATION
IV. EVALUATING CORE INFLATION INDICATORS
V. CONCLUDING REMARKS
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I. INTRODUCTION
Reasons of using CORE INFLATION indicators : -- inflation targeting strategy -- better controlled by the monetary authority -- good predictor of future inflation
CORE INFLATION= the persistent component; the trend of CPI inflation; the common component of all prices
Different definitions of core inflation different methods of estimation.
GOAL: estimating and choosing the best core inflation measure for Romania, considering the established criteria
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1. Central-bank approacha) “Zero-weighting” technique
• often used in practice and easy explainable to the public• excludes volatile items of CPI: administrated prices, seasonal or interest rate sensitive components
• disadvantage: arbitrary basis in removing CPI items
b) Trimmed mean method (Bryan &Cecchetti-1994)
• argument : distribution of individual price change is skewed & leptokurtic
• cuts % from both tails of price change distribution• theoretical model: price setting with costly price adjustment (Ball & Mankiw -1994)
II. THEORETICAL BACKGROUND
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Core inflation= persistent component of measured price index, which is tied in some way to money growth (Bryan &Cecchetti - 1994,1997)
core=m*
i firms where ei (shock in production costs) exceeds the
“menu costs”: i=m*+ei
The change of aggregate price level depends on the shape of shocks (supply shocks) distribution:
- symmetricalCPI inflation= c - asymmetricalCPI inflation> or< core
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2. Quah & Vahey approach and extensions
Core inflation= the component of measured inflation that has no impact on real output in the medium-long run (Quah & Vahey -1995).
on the basis of vertical long run Phillips Curve
• placing long- run restrictions on a VAR system in: real output and inflation
• Blachard& Quah decomposition for identifying the 2 structural shocks: -- non-core shock
-- core shock
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Identification steps:
Step 1: Reduced form VAR in first differences of real output & CPI : Xt =+ B(L)et , var(et) =ee’=
Step 2: Xt = +C(L)t, var(t) = ; Cot = et; CoCo’ = Step 3: Identifying Co:• orthogonality and unit variance of t: n(n+1)/2 restrictions.• n(n-1)/2 long run restrictions C(1) triangular Step 4: Core inflation recovered considering non-core zero recomputed shocks from t = Co-1 et. For 2 variables:
...
..
2221
1211
0 jtcore
jtnoncore
jcjc
jcjc
Pt
Yt
j
jtcorejcccorejcjj
..*2201200
Long run restriction:
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Extensions of Quah & Vahey method
• more variables: adding a monetary indicator
• Core shocks: -- monetary shocks -- real demand shocks
• Blix(1995),Fase&Folkertsma (2002)monetary aggregate
• Gartner & Wehinger (1998), Dewachter & Lustig(1997) short term interest rate
jtdemjcjtmonjcccorejcjcjcjj jjj
.*33.*32023,013,01200 000
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III. DATA AND ECONOMETRIC ESTIMATIONSAMPLE 1996:01 - 2002:12
Lxy is natural logarithm of xy variable ( LCPI = ln(CPI)); DLxy is the first difference of Lxy ( DLCPI(t) = LCPI(t) – LCPI(t-1) is the monthly inflation rate). Ixy index as against January 1996)
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ESTIMATION RESULTS:1. “Zero - weighting” methodCORE0
Excluded items (26.27% of CPI basket): • Administrated prices (18.77%) - electric energy, gas, central heating - water, salubrity - mail & telecommunications - urban & interurban transport • Seasonal prices (7.5%) - fruits & tinned fruits - vegetables & tinned vegetables
0
4
8
12
16
20
24
28
32
1996 1997 1998 1999 2000 2001 2002
CORE0 CPI inflation
%
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2. Trimmed mean estimationTRIM
0
5
10
15
20
25
30
0.00 0.05 0.10 0.15 0.20 0.25
Series: DLCPISample 1996:01 2002:12Observations 84
Mean 0.034762Median 0.025760Maximum 0.267542Minimum 0.003860Std. Dev. 0.036256Skewness 4.104993Kurtosis 23.98776
Jarque-Bera 1777.615Probability 0.000000
DLCPI (CPI inflation) series
• highly asymmetric and leptokurtic inflation distribution• Average weighted skewness=1.0439• Average weighted kurtosis = 19.784
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• Symmetric trimming: 5%, 10%, 15%, 18%, 30%• Trimming a higher percent more stable indicator of core
inflation
-5
0
5
10
15
20
25
30
1996 1997 1998 1999 2000 2001 2002
CPI inflationTRIM5TRIM10TRIM15TRIM18TRIM30
%2. Trimmed mean estimationTRIM
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3. Quah & Vahey approachCORE
a) SVAR 1: DLY_SA, DLCPI and a constantCORE2
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SVAR1 tests: stability, lag length & residuals
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
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-30
-20
-10
0
10
20
30
1996 1998 1999 2000 2001 2002
CUSUM 5% Significance
-30
-20
-10
0
10
20
30
1996 1998 1999 2000 2001 2002
CUSUM 5% Significance
.00
.05
.10
.15
-.04
-.02
.00
.02
.04
1996 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
.00
.05
.10
.15-.08
-.04
.00
.04
.08
1996 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
Parameters stability tests:Eq. DLY_SA Eq. DLCPI
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b) SVAR 2: DLY_SA,DLCPI,constant & Dummy March 1997CORE2d
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
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-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1997 1998 1999 2000 2001 2002
CUSUM of Squares 5% Significance
.00
.05
.10
.15-.04
-.02
.00
.02
.04
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1997 1998 1999 2000 2001 2002
CUSUM of Squares 5% Significance
.00
.05
.10
.15-.04
-.02
.00
.02
.04
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
SVAR2 parameters stability:Eq DLY_SA Eq DLCPI
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b) SVAR 3: DLY_SA, DLM2_SA, DLCPI, constant CORE3
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
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Parameters stability tests:Eq DLY_SA Eq DLM2_SA Eq DLCPI
-30
-20
-10
0
10
20
30
1997 1998 1999 2000 2001 2002
CUSUM 5% Significance
-30
-20
-10
0
10
20
30
1997 1998 1999 2000 2001 2002
CUSUM 5% Significance
-30
-20
-10
0
10
20
30
1997 1998 1999 2000 2001 2002
CUSUM 5% Significance
.00
.05
.10
.15-.04
-.02
.00
.02
.04
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
.00
.05
.10
.15
-.08
-.04
.00
.04
.08
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
.00
.05
.10
.15-.08
-.04
.00
.04
.08
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
CHSQ(1) =0.831 [0.361]; CHSQ(1)=1.130 [0.252]; CHSQ(1)=0.104 [0.745] (Ramsey RESET test 1 fitted term)
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b) SVAR 4: DLY_SA, DLM2_SA, DLCPI, constant, Dummy March 1997 CORE3d
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
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Parameters stability tests:Eq DLY_SA Eq DLM2_SA Eq DLCPI
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1997 1998 1999 2000 2001 2002
CUSUM of Squares 5% Significance
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1997 1998 1999 2000 2001 2002
CUSUM of Squares 5% Significance
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1997 1998 1999 2000 2001 2002
CUSUM of Squares 5% Significance
.00
.05
.10
.15-.04
-.02
.00
.02
.04
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
.00
.05
.10
.15-.08
-.04
.00
.04
.08
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
.00
.05
.10
.15-.06
-.04
-.02
.00
.02
.04
.06
1997 1998 1999 2000 2001 2002
N-Step Probability Recursive Residuals
CHSQ=1.718 [0.189] CHSQ=2.180 [0.139] CHSQ=0.458 [0.497] (Ramsey RESET test 1 fitted term)
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IV. EVALUATING CORE INFLATION INDICATORSA) Quah & Vahey core inflation measures & economic content
SVAR1 CORE2
0
4
8
12
16
20
24
28
32
1996 1997 1998 1999 2000 2001 2002
CPI inflation CORE2 -SSV
VA
AR
R1
1))
%
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.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
Accumulated Response of DLY_SA to ShockNON-CORE
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
Accumulated Response of DLY_SA to Shock CORE
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Accumulated Response of DLCPI to Shock NON-CORE
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Accumulated Response of DLCPI to Shock CORE
Accumulated Response to Structural One S.D. Innovations
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
Shock NON-CORE Shock CORE
Variance Decomposition of DLY_SA
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
Shock NON-CORE Shock CORE
Variance Decomposition of DLCPI
Non-core shocks supply shocks;Core shocks demand shocks
96%
88%
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SVAR2 CORE2d
0
4
8
12
16
20
24
28
32
1996 1997 1998 1999 2000 2001 2002
CPI inflation CORE2d (SVAR2)
%
• strong inertial character of
inflation • administrated & seasonal prices or supply shocks are not determinant inflationary sources
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SVAR3 CORE3
-5
0
5
10
15
20
25
30
35
1996 1997 1998 1999 2000 2001 2002
CPI inflation CORE3 (SVAR 3)
%
60
80
100
120
140
160
1996 1997 1998 1999 2000 2001 2002
Y_SA WL_SA EMPL
%
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LNONCORE3= DLCPI - LCORE3-3
-2
-1
0
1
2
3
4
1996 1997 1998 1999 2000 2001 2002
NONCORE3
%
Test statistics: 1. Serial correlation LM: F-statistic 0.593 [0.837]; Obs*R-squared 7.229 [0.842] 2. White heteroskedasticity: F-statistic 0.595 [0.857]; Obs*R-squared 9.168 [0.820][ [ ] P-VALUE 3. Ramsey’s test (2 fitted): F-statistic 0.042 [0.958]; Loglikelihood ratio 0.098 [0.951] 4. Normality: Jarque-Bera 0.777168 [0.678016]
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SVAR4 CORE3d
-4
0
4
8
12
16
20
24
28
32
1996 1997 1998 1999 2000 2001 2002
CPI inflation CORE3d (SVAR4)
%
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B) Choosing the best core inflation indicator
CRITERIA: Bryan & Cecchetti (1994), Roger(1997), Marques (2000),
Valkovszky & Vincze(2000), H. Mio (2001)
1. Core & CPI inflation correlation2. Cointegration condition3. Moving average methods & efficient core indicators4. Core measures & the correlation with money growth
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1. Core & CPI inflation correlation
• Correlation coefficients: higher for TRIM• Granger causality tests DLCPI - CORE indicators
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2. Cointegration condition
LICPI96=0.884023*LICORE3+0.257784 Speed of adjustment (-0.114694, –0.099032)
Long run relation (4 lags in differences):
LICPI96 & LICORE3 (log of index base Jan. ‘96)
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3. Moving average methods & efficient core indicators
n
itt MACORE
NRMSE
1
2)(1
n
itt MACORE
nMAD
1
1
TRIM18 - The best core indicatorCORE3 - the best among Quah & Vahey core indicators
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4. Core measures & the correlation with money growth
• Granger causality tests CORE measures - DLM2_SA - Core should be Granger caused by money growth & not reverse
TRIM18 performs better in the long run
• Inflation indicators variability
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V. CONCLUDING REMARKS
• Core inflation indicators closely follow the CPI inflation
• Decreasing variability of TRIM & Exclusion methods;
• TRIM18 would be recommended as the optimal core indicator
• Quah & Vahey indicators perform less successful, but are signaling links in economic variables