1. Dissertation Paper Student: ANGELA-MONICA MĂRGĂRIT Supervisor: Professor MOISĂ ALTĂR July 2003 ACADEMY OF ECONOMIC STUDIES BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING

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. 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) II. THEORETICAL BACKGROUND

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 firms where e i (shock in production costs) exceeds the “menu costs”:  i=m*+e i  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’=  Step 2: Xt =  +C(L)  t, var(  t) =  ; Co  t = 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: 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. Symmetric trimming: 5%, 10%, 15%, 18%, 30% Trimming a higher percent  more stable indicator of core inflation 2. Trimmed mean estimation  TRIM

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=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 )

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