Asymmetric Conjugate Priors for Large Bayesian VARs

Joshua Chan

Papers from arXiv.org

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 modeling flexibility, 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. We demonstrate the usefulness of the new prior via a structural analysis using a 15-variable VAR with sign restrictions to identify 5 structural shocks.

Date: 2021-11
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (3)

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Journal Article: Asymmetric conjugate priors for large Bayesian VARs (2022)
Working Paper: Asymmetric conjugate priors for large Bayesian VARs (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2111.07170

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