Computationally efficient inference in large Bayesian mixed frequency VARs

Deborah Gefang, Gary Koop and Aubrey Poon

Economics Letters, 2020, vol. 191, issue C

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.

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)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Related works:
Working Paper: Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs (2020)
Working Paper: Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:191:y:2020:i:c:s0165176520301014

DOI: 10.1016/j.econlet.2020.109120

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