Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates

Luis Melo-Velandia, Rubén Loaiza Maya and Mauricio Villamizar-Villegas

Borradores de Economia from Banco de la Republica de Colombia

Abstract: Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon.

JEL-codes: C11 C22 C53 E31 (search for similar items in EconPapers)
Pages: 21
Date: 2014-11
New Economics Papers: this item is included in nep-for, nep-mac and nep-mon
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https://doi.org/10.32468/be.853 (application/pdf)

Related works:
Working Paper: Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates (2019)
Journal Article: Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates (2016)
Working Paper: Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:bdr:borrec:853

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