Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates
Authors
Melo-Velandia, Luis Fernando
Loaiza, Rubén
Villamizar-Villegas, Mauricio
Editor
Publication date
2014-11-20
Document language
eng
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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.
Description
Códigos JEL
C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion processes, C53 - Forecasting and Prediction Methods; Simulation Methods, C11 - Bayesian Analysis: General, E31 - Price Level; Inflation; Deflation
item.page.subjectjelspa
C11 - Análisis bayesiano: generalidades, C22 - Modelos de series temporales; Regresiones cuantiles dinámicas; Modelos dinámicos de tratamiento; procesos de difusión, C53 - Métodos de pronóstico y predicción; métodos de simulación, E31 - Nivel de precios; Inflación; Deflación
Keywords
Bayesian shrinkage, Inflation forecast combination, Internal forecasts, Rolling window estimation
