Bayesian compressed vector autoregressions

Gary Koop, Dimitris Korobilis, Davide Pettenuzzo

Research output: Contribution to journalArticle

12 Citations (Scopus)
29 Downloads (Pure)

Abstract

Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up
to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.
Original languageEnglish
Pages (from-to)135-154
Number of pages20
JournalJournal of Econometrics
Volume210
Issue number1
Early online date12 Nov 2018
DOIs
Publication statusPublished - 31 May 2019

Keywords

  • multivariate time series
  • random projection
  • forecasting
  • vector autoregressions

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