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MEASURING CORE INFLATION IN ROMANIA

ACADEMY OF ECONOMIC STUDIES BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING. MEASURING CORE INFLATION IN ROMANIA. Dissertation Paper Student: ANGELA-MONICA MĂRGĂRIT Supervisor: Prof essor MOIS Ă ALTĂR. July 2003. I. INTRODUCTION II. THEORETICAL BACKGROUND

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MEASURING CORE INFLATION IN ROMANIA

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  1. ACADEMY OF ECONOMIC STUDIES BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING MEASURING CORE INFLATION IN ROMANIA Dissertation Paper Student: ANGELA-MONICAMĂRGĂRIT Supervisor: Professor MOISĂ ALTĂR July 2003

  2. I. INTRODUCTION II. THEORETICAL BACKGROUND III. DATA AND ECONOMETRIC ESTIMATION IV. EVALUATING CORE INFLATION INDICATORS V. CONCLUDING REMARKS

  3. 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

  4. 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 inremoving 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)

  5. Core inflation= persistent component of measured price index, which is tied in some way to money growth(Bryan &Cecchetti - 1994,1997) • core=m* i firmswhere 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: - symmetricalCPI inflation= c - asymmetricalCPI inflation> or<core

  6. 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

  7. 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(n+1)/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:

  8. 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

  9. III. DATA AND ECONOMETRIC ESTIMATION SAMPLE 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)

  10. 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

  11. 2. Trimmed mean estimationTRIM DLCPI (CPI inflation) series • highly asymmetric and leptokurtic inflation distribution • Average weighted skewness=1.0439 • Average weighted kurtosis = 19.784

  12. 2. Trimmed mean estimationTRIM • Symmetric trimming: 5%, 10%, 15%, 18%, 30% • Trimming a higher percent more stable indicator of core inflation

  13. 3. Quah & Vahey approachCORE a) SVAR 1: DLY_SA, DLCPI and a constantCORE2

  14. SVAR1 tests: stability, lag length & residuals

  15. Parameters stability tests: Eq. DLY_SA Eq. DLCPI

  16. b) SVAR 2: DLY_SA,DLCPI,constant & Dummy March 1997CORE2d

  17. SVAR2 parameters stability: Eq DLY_SA Eq DLCPI

  18. b) SVAR 3: DLY_SA, DLM2_SA, DLCPI, constant CORE3

  19. 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)

  20. b) SVAR 4: DLY_SA, DLM2_SA, DLCPI, constant, Dummy March 1997 CORE3d

  21. Parameters stability tests: Eq DLY_SA Eq DLM2_SA Eq DLCPI CHSQ=1.718 [0.189] CHSQ=2.180 [0.139] CHSQ=0.458 [0.497] (Ramsey RESET test 1 fitted term)

  22. IV. EVALUATING CORE INFLATION INDICATORS A) Quah & Vahey core inflation measures & economic content SVAR1 CORE2

  23. Non-core shocks supply shocks; Core shocks demand shocks 96% 88%

  24. SVAR2  CORE2d • strong inertial character of inflation • administrated & seasonal prices or supply shocks are not determinant inflationary sources

  25. SVAR3  CORE3

  26. LNONCORE3= DLCPI - LCORE3 • 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]

  27. SVAR4  CORE3d

  28. 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

  29. 1. Core & CPI inflation correlation • Correlation coefficients: higher for TRIM • Granger causality tests DLCPI - CORE indicators

  30. 2. Cointegration condition LICPI96 & LICORE3 (log of index base Jan. ‘96) Long run relation (4 lags in differences): LICPI96=0.884023*LICORE3+0.257784 Speed of adjustment (-0.114694, –0.099032)

  31. 3. Moving average methods & efficient core indicators TRIM18 - The best core indicator CORE3 - the best among Quah & Vahey core indicators

  32. 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

  33. 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

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