Computationally efficient inference in large Bayesian mixed frequency VARs

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Gefang, Deborah and Koop, Gary and Poon, Aubrey (2020) Computationally efficient inference in large Bayesian mixed frequency VARs. Economics Letters, 191. 109120. ISSN 0165-1765 (https://doi.org/10.1016/j.econlet.2020.109120)

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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.

ORCID iDs

Gefang, Deborah, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Poon, Aubrey ORCID logoORCID: https://orcid.org/0000-0003-2587-8779;
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