Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage

Deborah Gefang, Gary Koop and Aubrey Poon

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

Abstract: Many recent papers in macroeconomics have used large Vector Autoregressions (VARs) involving a hundred or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital in achieving reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayes methods for large VARs which overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.

Keywords: 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: 2019-03
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (13)

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Related works:
Working Paper: Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage (2019)
Working Paper: Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage (2019)
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