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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
I. INTRODUCTION II. THEORETICAL BACKGROUND
III. DATA AND ECONOMETRIC ESTIMATION
IV. EVALUATING CORE INFLATION INDICATORS
V. CONCLUDING REMARKS
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
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
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
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
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:
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
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)
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
%
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
• 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
3. Quah & Vahey approachCORE
a) SVAR 1: DLY_SA, DLCPI and a constantCORE2
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
-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
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
-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
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
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)
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
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)
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))
%
.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%
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
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
%
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]
SVAR4 CORE3d
-4
0
4
8
12
16
20
24
28
32
1996 1997 1998 1999 2000 2001 2002
CPI inflation CORE3d (SVAR4)
%
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
1. Core & CPI inflation correlation
• Correlation coefficients: higher for TRIM• Granger causality tests DLCPI - CORE indicators
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)
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
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
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