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

  • Luis Fernando Melo


  • Rubén Albeiro Loaiza Maya


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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Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 705.

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Length: 18
Date of creation: Apr 2012
Date of revision:
Handle: RePEc:bdr:borrec:705
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  1. 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, June.
  2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
  3. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
  4. Gary Koop & Simon M. Potter, 2003. "Forecasting in large macroeconomic panels using Bayesian Model Averaging," Staff Reports 163, Federal Reserve Bank of New York.
  5. Zellner, A. & Hong, C., 1988. "Forecasting International Growth Rates Using Bayesian Shrinkage And Other Procedures," Papers m8802, Southern California - Department of Economics.
  6. Luis Fernando melo V. & Martha Misas A., . "Modelos Estructurales de Inflación en Colombia: Estimación a traves de Minimos Cuadrados Flexibles," Borradores de Economia 282, Banco de la Republica de Colombia.
  7. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
  8. Miguel I. Gómez & Eliana R. González & Luis F. Melo, 2012. "Forecasting Food Inflation in Developing Countries with Inflation Targeting Regimes," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 153-173.
  9. Munir A. Jalil B. & Luis Fernando Melo Velandia, 2000. "Una Relación No Lineal Entre Inflación Y Medios De Pago," BORRADORES DE ECONOMIA 003725, BANCO DE LA REPÚBLICA.
  10. Luis Fernando Melo Velandia & Héctor M. Núñez Amortegui, 2004. "Combinación de pronósticos de la inflación en presencia de cambios estructurales," BORRADORES DE ECONOMIA 002153, BANCO DE LA REPÚBLICA.
  11. Munir A. Jalil & Luis Fernando Melo, . "Una Relación no Líneal entre Inflación y los Medios de Pago," Borradores de Economia 145, Banco de la Republica de Colombia.
  12. Elkin Castaño Vélez & Luis Fernando Melo Velandia, 2000. "Metodos de combinacion de pronosticos: una aplicacion a la inflacion," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 52, pages 113-165, Enero Jun.
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