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 dataset 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. Typically, we find that the simple Minnesota prior forecasts well in medium and large VARs, which makes this prior attractive relative to computationally more demanding alternatives. Our empirical results show the importance of using forecast metrics based on the entire predictive density, instead of relying solely on those based on point forecasts
Original languageEnglish
Number of pages27
JournalJournal of Applied Econometrics
Early online date17 Oct 2011
DOIs
Publication statusPublished - 2012

Keywords

  • Bayesian VARs
  • forecasting
  • priors
  • small VARs
  • computational issues
  • conditionally conjugate
  • VAR forecasting
  • empirical study

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