Inference in Bayesian Proxy-SVARs

Jonas E. Arias, Juan F. Rubio-Ramírez, and Daniel F. Waggoner
Working Paper 2018-16
December 2018

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Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy-SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often imposed to facilitate inference when more than one instrument are used to identify more than one equation, as in Mertens and Montiel-Olea (2018).

JEL classification: C15, C32

Key words: SVARs, external instruments, importance sampler

The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of Philadelphia, or the Federal Reserve System. Any remaining errors are the authors' responsibility. No statements here should be treated as legal advice.
Please address questions regarding content to Jonas E. Arias, Research Department, Federal Reserve Bank of Philadelphia, Ten Independence Mall, Philadelphia, PA 19106-1574,; Juan F. Rubio-Ramírez, Department of Economics, Emory University, Rich Memorial Building, Room 306, Atlanta, GA 30322-2240 and Federal Reserve Bank of Atlanta and BBVA Research,; or Daniel F. Waggoner, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, 404-498-8278,
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