Forecasting with Medium and Large Bayesian VARS

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Abstract

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Pages1-34
Number of pages35
Volume2011
Publication statusPublished - Feb 2010

Keywords

  • bayesian
  • Minnesota prior
  • stochastic search variable selection
  • predictive likelihood

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