Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates

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 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.