Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs

Deborah Gefang, Gary Koop and Aubrey Poon

Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)

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 overparameterization 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: 2020-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (13)

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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
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