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This is, however, at the expense of modeling flexibility, as it rules out cross\u2010variable shrinkage, that is, 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\u2010variable shrinkage, while maintaining many useful analytical results, such as a closed\u2010form expression of the marginal likelihood. This new prior also leads to fast posterior simulation\u2014for 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\u2010variable VAR with sign restrictions to identify 5 structural shocks.<\/jats:p>","DOI":"10.3982\/qe1381","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T05:56:45Z","timestamp":1657173405000},"page":"1145-1169","source":"Crossref","is-referenced-by-count":43,"title":["Asymmetric conjugate priors for large Bayesian VARs"],"prefix":"10.3982","volume":"13","author":[{"given":"Joshua C. 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