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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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  • Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:545
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    File URL: https://www.bcentral.cl/documents/33528/133326/DTBC_545.pdf
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    Citations

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    Cited by:

    1. Carlos Medel, 2012. "¿Akaike o Schwarz? ¿Cuál elegir para Predecir el PIB Chileno?," Working Papers Central Bank of Chile 658, Central Bank of Chile.
    2. Alberto Naudon, 2010. "A Stochastic Assignment Model," Working Papers Central Bank of Chile 558, Central Bank of Chile.
    3. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, CEMLA, vol. 0(4), pages 429-459, octubre-d.
    4. 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.
    5. Rodrigo A. Alfaro & Rodrigo Cifuentes S., 2011. "Financial Stability, Monetary Policy, and Central Banking: An Overview," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 1, pages 001-010, Central Bank of Chile.
    6. 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.
    7. David Aikman & Piergiorgio Alessandri & Bruno Eklund & Prasanna Gai & Sujit Kapadia & Elizabeth Martin & Nada Mora & Gabriel Sterne & Matthew Willison, 2011. "Funding Liquidity Risk in a Quantitative Model of Systemic Stability," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 12, pages 371-410, Central Bank of Chile.
    8. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    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. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, CEMLA, vol. 0(4), pages 591-615, octubre-d.
    11. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    12. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Money Affairs, CEMLA, vol. 0(1), pages 37-73, January-J.
    13. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, CEMLA, vol. 0(4), pages 461-515, octubre-d.

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