Large Bayesian VARMAs

Joshua C C Chan, Eric Eisenstat, Gary Koop

Research output: Working paperDiscussion paper

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Abstract

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Pages1-41
Number of pages42
Volume14
Publication statusPublished - 25 Sep 2014

Keywords

  • varma identification
  • markov chain monte carlo
  • bayesian
  • stochastic search variable selection

Cite this

Chan, J. C. C., Eisenstat, E., & Koop, G. (2014). Large Bayesian VARMAs. (09 ed.) (pp. 1-41). University of Strathclyde.