Asymmetric conjugate 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 are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modelling exibility, as it rules out cross-variable shrinkage – i.e. shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross-variable shrinkage, while maintaining many useful analytical results, such as a closed-form expression of the marginal likelihood. This new prior also leads to fast posterior simulation - for a BVAR with 100 variables and 4 lags, obtaining 10,000 posterior draws takes less than half a minute on a standard desktop. In a forecasting exercise, we show that a data-driven asymmetric prior outperforms two useful benchmarks: a data-driven symmetric prior and a subjective asymmetric prior.

Keywords: shrinkage prior; forecasting; marginal likelihood; optimal hyperparameters; structural VAR (search for similar items in EconPapers)
JEL-codes: C11 C52 C55 E37 E47 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2019-07
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Asymmetric conjugate priors for large Bayesian VARs (2022)
Working Paper: Asymmetric Conjugate Priors for Large Bayesian VARs (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2019-51

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