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Evaluating alternative measures of Core Inflation for Argentina

Evaluating alternative measures of Core Inflation for Argentina. Laura D’Amato Juan Sotes Paladino and Lidia Sanz BCRA VIII Annual Seminar, Central Bank of Brazil August 2006. Motivation. Argentina: return to active monetary policy after more than ten years under a hard peg

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Evaluating alternative measures of Core Inflation for Argentina

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  1. Evaluating alternative measures of Core Inflation for Argentina Laura D’Amato Juan Sotes Paladino and Lidia Sanz BCRA VIII Annual Seminar, Central Bank of Brazil August 2006

  2. Motivation • Argentina: return to active monetary policy after more than ten years under a hard peg • Have an adequate assessment of inflation trend crucial for monetary policy decisions • But CPI inflation influenced by transitory movements on relative prices • Core Inflation as a monetary phenomenon (Silver, 2006) • How to separate signal of inflation from temporary noise

  3. Motivation • Problem: dramatic change in RER due to large depreciation of the peso in January 2002 • Change in relative prices rather permanent • Strong initial impact of nominal depreciation on “core inflation” followed by very different paths of tradable and non-tradable goods prices • The cross section distribution of CPI components’ price changes suffered sharp alterations due to abrupt depreciation of the currency

  4. Tradable and Non-tradable goods prices

  5. Goods vs Services Prices

  6. Cross-section distribution of CPI components’ price changes (2002-2004)

  7. Cross-section distribution of CPI components’ price changes (2005-2006)

  8. Cross-section distribution of CPI components’ price changes (2002-2004)

  9. Outline • Core Inflation as a signal extraction problem • Exclusion-based methods • Limited influence estimators • Evaluation • Conclusions

  10. Core Inflation as a signal extraction problem Persistence-based reweighting • Blinder (1997): Core Inflation as the persistent component of CPI • Cutler (2001) reweigh the CPI using persistence based weights • Two weighting vectors: Jan-93/Jan04 and Jan-93/Oct-00

  11. Persistence based Core Inflation Index (CPIP)

  12. Core Inflation as a signal extraction problem Persistence based reweighting: Results • Food items, usually excluded, have high weight (meat, diary products and eggs) • Regulated Services lose weight due to sporadic changes in prices • Items with high imports component are excluded • Have to revise persistence weights

  13. Core Inflation as a signal extraction problem Volatility-based reweighting • Marques, Neves y Sarmento (2000): weights based on standard deviation of price changes of CPI components relative to mean CPI inflation

  14. Volatility-based Core Inflation Index (CPIV)

  15. Exclusion-based methods Systematic criteria: • Exclude Items simultaneously less persistent and more volatile and according to: • Variance of relative price changes (whole period) • Marginal contribution of each item to the volatility of CPI inflation • Ordering according to Principal Components Analysis

  16. Exclusion-based methods Systematic criteria: Criterion i : 11.7% of CPI components excluded, basically items with high imports component Criteria ii and iii: exclusion of almost all Regulated Services Items (14% of CPI)

  17. Exclusion-based methods: Ex F&E and Core INDEC

  18. Limited influence estimators Brian and Cechetti (1997): • Cross-section distribution of CPI components’ price changes non-normal and with heavy tails • Strong case for the use of robust estimators of the first moment of cross-section distribution • Cross-sectional distribution of 65 components of CPI • Truncate at the 7.5%. 15% and 20% level • Underestimates CPI inflation • Frequency of exclusion informative about volatility of CPI components’ price changes. Can use as exclusion criterion

  19. Limited influence estimators

  20. Evaluation If interested on Core Inflation for prediction: • Granger Causality • Poor results: only volatility-based index performs adequately • Marques et al (2001): Core inflation as an atractor • In Argentina CPI is I(0) • only persistence- based index performs rather well

  21. Conclusions • Persistence and volatility based indicators better performance for prediction • Exclusion indicators and limited influence estimators can be useful to have an assessment of current “core inflation” • Trimmed means probably useful when cross section distribution of price changes is unstable • Correct bias of trimmed means (Silver 2006) • Having more observations of new monetary regime more work needed. • Recalculate persistence weights • Principal component analysis for post-crisis period

  22. Recent evolution of Core Inflation measures

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