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

Luis Melo-Velandia and Rubén Loaiza Maya

Borradores de Economia from Banco de la Republica de Colombia

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.

Keywords: Forecast combination; Shrinkage; Expert forecasts; Rolling window estimation; Inflation forecasts. (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E31 (search for similar items in EconPapers)
Pages: 18
Date: 2012-04
New Economics Papers: this item is included in nep-cba, nep-for and nep-mac
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https://doi.org/10.32468/be.705 (application/pdf)

Related works:
Working Paper: Bayesian Forecast Combination for Inflation Using Rolling Windows: An Emerging Country Case (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:bdr:borrec:705

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