Computationally efficient inference in large Bayesian mixed frequency VARs

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)

[thumbnail of Gefang-etal-EL-2020-Computationally-efficient-inference-in-large-Bayesian-mixed-frequency-VARs]
Preview
 Text. Filename: Gefang_etal_EL_2020_Computationally_efficient_inference_in_large_Bayesian_mixed_frequency_VARs.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (392kB)| Preview

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.

CORE (COnnecting REpositories)