Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Joshua Chan

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to make these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like the adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors, such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors - for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 minutes on a standard desktop. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.

Keywords: shrinkage prior; forecasting; stochastic volatility; structural VAR (search for similar items in EconPapers)
JEL-codes: C11 C52 C55 E37 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2019-08
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
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Journal Article: Minnesota-type adaptive hierarchical priors for large Bayesian VARs (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2019-61

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