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First, we show how to determine which conditioning variables span the relevant information by directly accounting for the Beveridge\u2013Nelson trend and cycle in terms of contributions from different forecast errors. Second, we employ Bayesian shrinkage to avoid overfitting in finite samples when estimating models that are large enough to include many possible sources of information. An empirical application with up to 138 variables covering various aspects of the US economy reveals that the unemployment rate, inflation, and, to a lesser extent, housing starts, aggregate consumption, stock prices, real money balances, and the federal funds rate contain relevant information beyond that in output growth for estimating the output gap, with estimates largely robust to substituting some of these variables or incorporating additional variables.<\/jats:p>","DOI":"10.1002\/jae.2733","type":"journal-article","created":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T16:53:51Z","timestamp":1565110431000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Estimating and accounting for the output gap with large Bayesian vector autoregressions"],"prefix":"10.1002","volume":"35","author":[{"given":"James","family":"Morley","sequence":"first","affiliation":[{"name":"School of Economics University of Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Econometrics and Business Statistics Monash University  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,11,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.659"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.1137"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"Barigozzi M. &Luciani M.(2018).Measuring US aggregate output and output gap using large datasets. 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