Inference in Bayesian Proxy-Svars

48 Pages Posted: 5 Feb 2019 Last revised: 21 Feb 2019

See all articles by Jonas Arias

Jonas Arias

Federal Reserve Bank of Philadelphia

Juan Rubio Ramírez

Emory University

Daniel F. Waggoner

Federal Reserve Bank of Atlanta

Multiple version iconThere are 2 versions of this paper

Date Written: 2018-12-01

Abstract

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).

Keywords: SVARs, external instruments, importance sampler

JEL Classification: C15, C32

Suggested Citation

Arias, Jonas and Rubio Ramírez, Juan and Waggoner, Daniel F., Inference in Bayesian Proxy-Svars (2018-12-01). FRB Atlanta Working Paper No. 2018-16. Available at SSRN: https://ssrn.com/abstract=3329135 or http://dx.doi.org/10.29338/wp2018-16

Jonas Arias (Contact Author)

Federal Reserve Bank of Philadelphia ( email )

10 N Independence Mall W
Philadelphia, PA 19106
United States

Juan Rubio Ramírez

Emory University ( email )

201 Dowman Drive
Atlanta, GA 30322
United States

Daniel F. Waggoner

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States
404-521-8278 (Phone)

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