Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs

Deborah Gefang, Gary Koop and Aubrey Poon

No 20/02, Discussion Papers in Economics from Division of Economics, School of Business, University of Leicester

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 effcient than MCMC in two empirical applications involving large MF-VARs.

Keywords: Mixed Frequency; Variational inference; Vector Autoregression; Stochastic Volatility; Hierarchical Prior; Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
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Related works:
Journal Article: Computationally efficient inference in large Bayesian mixed frequency VARs (2020)
Working Paper: Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs (2020)
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