Computationally efficient inference in large Bayesian mixed frequency VARs

Deborah Gefang, Gary Koop, Aubrey Poon

Research output: Contribution to journalArticlepeer-review


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
JournalEconomics Letters
Publication statusAccepted/In press - 25 Mar 2020


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


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