MEASURING CORE INFLATION IN ROMANIA

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ACADEMY OF ECONOMIC STUDIES BUCHAREST DOCTORAL
SCHOOL OF FINANCE AND BANKING
MEASURING CORE INFLATION IN ROMANIA
Dissertation Paper Student ANGELA-MONICA
MARGARIT Supervisor Professor MOISA ALTAR
July 2003
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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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II. THEORETICAL BACKGROUND
1. Central-bank approach
a) 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)

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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)
?corem
i firms where ei (shock in production costs)
exceeds the menu costs ?imei
?The change of aggregate price level depends on
the shape of shocks (supply shocks)
distribution -
symmetrical?CPI inflation ?c
- asymmetrical?CPI inflationgt orlt ?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 W
• Step 2 Xt ?C(L)et, var(et) I Coet
  et CoCo W
• Step 3 Identifying Co
• orthogonality and unit variance of et n(n1)/2
  restrictions.
• n(n-1)/2 long run restrictions ?C(1) triangular
• Step 4 Core inflation recovered considering e
  non-core zero
• ? recomputed shocks from et Co-1 et. For 2
  variables

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),FaseFolkertsma (2002)?monetary
  aggregate
• Gartner Wehinger (1998), Dewachter
  Lustig(1997)
• ? short term interest rate

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III. DATA AND ECONOMETRIC ESTIMATION SAMPLE
199601 - 200212
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
method?CORE0

• 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

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2. Trimmed mean estimation?TRIM
DLCPI (CPI inflation) series

• highly asymmetric and leptokurtic inflation
  distribution
• Average weighted skewness1.0439
• Average weighted kurtosis 19.784

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2. Trimmed mean estimation?TRIM

• Symmetric trimming 5, 10, 15, 18, 30
• Trimming a higher percent? more stable indicator
  of core inflation

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3. Quah Vahey approach?CORE
a) SVAR 1 DLY_SA, DLCPI and a constant?CORE2
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SVAR1 tests stability, lag length residuals
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Parameters stability tests Eq. DLY_SA
Eq. DLCPI
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b) SVAR 2 DLY_SA,DLCPI,constant Dummy March
1997?CORE2d
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SVAR2 parameters stability Eq DLY_SA
Eq DLCPI
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b) SVAR 3 DLY_SA, DLM2_SA, DLCPI, constant ?CORE3
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Parameters stability tests Eq DLY_SA Eq
DLM2_SA Eq DLCPI
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
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Parameters stability tests Eq DLY_SA Eq
DLM2_SA Eq DLCPI
CHSQ1.718 0.189 CHSQ2.180 0.139
CHSQ0.458 0.497 (Ramsey RESET test 1 fitted
term)
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IV. EVALUATING CORE INFLATION INDICATORS
A) Quah Vahey core inflation measures
economic content
SVAR1? CORE2
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Non-core shocks? supply shocks Core shocks?
demand shocks
96
88
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SVAR2 ? CORE2d

• strong inertial character of inflation
• administrated seasonal prices or supply shocks
  are not determinant inflationary sources

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SVAR3 ? CORE3
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LNONCORE3 DLCPI - LCORE3
Test statistics 1. Serial correlation LM
F-statistic 0.593 0.837 ObsR-squared
7.229 0.842 2.
White heteroskedasticity F-statistic 0.595
0.857 ObsR-squared 9.168 0.820
P-VALUE 3. Ramseys 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
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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 correlation 2.
Cointegration condition 3. Moving average methods
efficient core indicators 4. 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 LICORE3 (log of index base Jan. 96)
Long run relation (4 lags in differences)
LICPI960.884023LICORE30.257784 Speed of
adjustment (-0.114694, 0.099032)
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3. Moving average methods efficient core
indicators
TRIM18 - The best core indicator CORE3 - 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