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Forecasting Chilean Inflation From Disaggregate Components

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  • Marcus Cobb

Abstract

In this paper an exercise is performed to determine the usefulness of utilizing disaggregated price data to forecast headline inflation more accurately. A number of methods based on univariate and multivariate autoregressive models are used for different levels of disaggregation for a period of stable inflation and a period of accelerating inflation. The results show that a certain level of disaggregation could be beneficial when inflation is not low and stable, suggesting that under certain circumstances the disaggregate approach captures the underlying dynamics of inflation more efficiently. The benefits are noticeable for the three-, six- and twelve-month horizons, as opposed to the one-month horizon, where improvements seem negligible.

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Bibliographic Info

Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 545.

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Date of creation: Dec 2009
Date of revision:
Handle: RePEc:chb:bcchwp:545

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  1. James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
  2. Pär Österholm & Erik Hjalmarsson, 2007. "Testing for Cointegration Using the Johansen Methodology When Variables Are Near-Integrated," IMF Working Papers 07/141, International Monetary Fund.
  3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  4. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
  5. Aigner, Dennis J & Goldfeld, Stephen M, 1974. "Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components," Econometrica, Econometric Society, vol. 42(1), pages 113-34, January.
  6. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, School of Economics and Management, University of Aarhus.
  7. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 32-44.
  8. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-52, April.
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Cited by:
  1. Medel, Carlos A., 2012. "¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno?
    [Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?]
    ," MPRA Paper 35950, University Library of Munich, Germany.
  2. Rodrigo Alfaro A. & Rodrigo Cifuentes S., 2009. "Financial Stability, Monetary Policy and Central Banking: An Overview," Working Papers Central Bank of Chile 554, Central Bank of Chile.
  3. Alberto Naudon, 2010. "A Stochastic Assignment Model," Working Papers Central Bank of Chile 558, Central Bank of Chile.
  4. Carlos Medel & Marcela Urrutia, 2010. "Proyección Agregada y Desagregada del PIB Chileno con Procedimientos Automatizados de Series de Tiempo," Working Papers Central Bank of Chile 577, Central Bank of Chile.
  5. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Working Papers Central Bank of Chile 556, Central Bank of Chile.
  6. David Aikman & Piergiorgio Alessandri & Bruno Eklund & Prasanna Gai & Sujit Kapadia & Elizabeth Martin & Nada Mora & Gabriel Sterne & Matthew Willison, 2009. "Funding Liquidity Risk in a Quantitative Model of Systemic Stability," Working Papers Central Bank of Chile 555, Central Bank of Chile.
  7. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 591-615, octubre-d.
  8. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 517-589, octubre-d.
  9. Pablo Pincheira & Hernán Rubio, 2010. "The Low Predictive Power of Simple Phillips Curves in Chile: A Real-Time Evaluation," Working Papers Central Bank of Chile 559, Central Bank of Chile.
  10. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 429-459, octubre-d.
  11. Carlos Medel, 2012. "How Informative are In–Sample Information Criteria to Forecasting? The Case of Chilean GDP," Working Papers Central Bank of Chile 657, Central Bank of Chile.
  12. Leon, Jorge, 2012. "A Disaggregate Model and Second Round Effects for the CPI Inflation in Costa Rica," MPRA Paper 44484, University Library of Munich, Germany, revised 2012.
  13. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 461-515, octubre-d.

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