Computationally efficient inference in large Bayesian mixed frequency VARs

Deborah Gefang, Gary Koop, Aubrey Poon

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
31 Downloads (Pure)

Abstract

Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome over-parameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet-Laplace global-local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.
Original languageEnglish
Article number109120
JournalEconomics Letters
Volume191
Early online date30 Mar 2020
DOIs
Publication statusPublished - Jun 2020

Keywords

  • mixed frequency
  • variational inference (VI)
  • vector autoregression
  • stochastic volatility
  • hierarchical prior
  • forecasting

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