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Bayesian Forecast Combination for Inflation Using Rolling Windows: An Emerging Country Case

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  • Luis Fernando Melo

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Abstract

Typically, when forecasting inflation rates, there are a variety of individual models and a combination of several of these models. We implement a Bayesian shrinkage combination methodology to include information that is not captured by the individual models using expert forecasts as prior information. To take into account two common characteristics in emerging countries´ economies, possible parameter instabilities and non-stationary dynamics, we use a rolling estimation windows technique for series integrated of order one. The empirical results of Colombian inflation show that the Bayesian forecast combination model outperforms the individual models and the random walk predictions for every evaluated forecast horizon. Moreover, these results outperform shrinkage forecasts that consider other priors as equal or zero weights.

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Bibliographic Info

Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 009511.

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Length: 30
Date of creation: 22 Apr 2012
Date of revision:
Handle: RePEc:col:000094:009511

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Keywords: Forecast combination; Shrinkage; Expert forecasts; Rolling window estimation; Inflation forecasts.;

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  1. Francis X. Diebold & Peter Pauly, 1987. "The use of prior information in forecast combination," Special Studies Papers 218, Board of Governors of the Federal Reserve System (U.S.).
  2. Luis Fernando Melo Velandia & Martha Alicia Misas Arango, 2004. "Modelos Estructurales de Inflación en Colombia: Estimación a través de Mínimos Cuadrados Flexibles," BORRADORES DE ECONOMIA 003244, BANCO DE LA REPÚBLICA.
  3. Martha Misas Arango & Enrique López Enciso & Pablo Querubín, 2002. "La Inflación En Colombia: Una Aproximación Desde Las Redes Neuronales," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  4. Zellner, Arnold & Hong, Chansik, 1989. "Forecasting international growth rates using Bayesian shrinkage and other procedures," Journal of Econometrics, Elsevier, vol. 40(1), pages 183-202, January.
  5. Luis Fernando Melo Velandia & Hugo Oliveros, 2004. "Combinación de pronósticos de la inflación en presencia de cambios estructurales," BORRADORES DE ECONOMIA 002153, BANCO DE LA REPÚBLICA.
  6. Gary Koop & Simon Potter, 2003. "Forecasting in large macroeconomic panels using Bayesian Model Averaging," Staff Reports 163, Federal Reserve Bank of New York.
  7. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
  8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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