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Forecasting Inflation in Chile With an Accurate Benchmark

  • Pablo Pincheira Brown
  • Álvaro García Marín

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.

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Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 514.

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Date of creation: Apr 2009
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Handle: RePEc:chb:bcchwp:514
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  1. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  2. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
  3. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
  4. 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.
  5. Patricio Jaramillo, 2008. "Estimación de Var Bayesianos para la Economía Chilena," Working Papers Central Bank of Chile 508, Central Bank of Chile.
  6. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  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, 02.
  8. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  9. Kenneth D. West & Whitney K. Newey, 1995. "Automatic Lag Selection in Covariance Matrix Estimation," NBER Technical Working Papers 0144, National Bureau of Economic Research, Inc.
  10. Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, Elsevier.
  11. Juan Díaz & Gustavo Leyva, 2008. "Forecasting Chilean Inflation in Difficult Times," Working Papers Central Bank of Chile 511, Central Bank of Chile.
  12. Giacomini, Raffaella & Rossi, Barbara, 2008. "Forecast Comparisons in Unstable Environments," Working Papers 08-04, Duke University, Department of Economics.
  13. 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.
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