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Forecasting Inflation in Chile With an Accurate Benchmark

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  • Pablo Pincheira Brown
  • Álvaro García Marín

Abstract

In this article we analyze the accuracy and stability of a number of inflation forecasts for Chile. We place special attention on forecasts coming from Extended Seasonal ARIMA (ESARIMA) models. Our analysis considers the sample period from January 2000 to November 2008. We compare ESARIMA forecasts to survey-based forecasts and to those coming from traditional time series benchmarks available in the literature. We consider one to six months ahead forecasts for comparisons with the traditional time series benchmarks and one, three and twelve month ahead forecasts for comparisons against the Survey of Professional Forecasters (SPF). Our results show that ESARIMA based forecasts display lower out-of-sample Mean Square Prediction Error than forecasts coming from traditional benchmarks, almost without exception. We obtain mixed results when comparing ESARIMA forecasts to survey-based forecasts in terms of forecast accuracy: the SPF provides more accurate forecasts for one month ahead forecasts, provides slightly less precise forecasts at the three month horizon and is less precise than half of our ESARIMA models at the 12 month horizon. Finally, we also note that our ESARIMA forecasts are more stable than traditional time series methods and also than survey based forecasts when prediction is made three and twelve months ahead.

Suggested Citation

  • Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
  • Handle: RePEc:chb:bcchwp:514
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    References listed on IDEAS

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    5. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    6. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    7. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
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    10. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    11. Juan Díaz & Gustavo Leyva, 2008. "Forecasting Chilean Inflation in Difficult Times," Working Papers Central Bank of Chile 511, Central Bank of Chile.
    12. Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, Elsevier.
    13. Patricio Jaramillo, 2009. "Estimación de Var Bayesianos para la Economía Chilena," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 24(1), pages 101-126, Junio.
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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. 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.
    3. Carlos Garcia & Pablo Gonzalez & Antonio Moncado, 2010. "Proyecciones Macroeconómicas en Chile: Una Aproximación Bayesiana," ILADES-Georgetown University Working Papers inv262, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines.
    4. Carlos J. García & Pablo González M. & Antonio Moncado S., 2013. "Macroeconomic Forecasting in Chile: a Structural Bayesian Approach," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 16(1), pages 24-63, April.
    5. 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.
    6. Pablo Pincheira, 2012. "A Joint Test of Superior Predictive Ability for Chilean Inflation Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 04-39, December.
    7. 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.
    8. 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.
    9. Idrovo Aguirre, Byron & Tejada, Mauricio, 2010. "Modelos de predicción para la inflación de Chile
      [Inflation forecast models for Chile]
      ," MPRA Paper 31586, University Library of Munich, Germany, revised 26 Mar 2010.
    10. Javier Contreras-Reyes & Byron Idrovo, 2011. "En busca de un modelo Benchmark univariado para predecir la tasa de desempleo," REVISTA CUADERNOS DE ECONOMÍA, UN - RCE - CID, December.

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