Inference in Bayesian Proxy-Svars

49 Pages Posted: 27 Nov 2018 Last revised: 21 Feb 2019

See all articles by Jonas Arias

Jonas Arias

Federal Reserve Bank of Philadelphia

Juan F. Rubio-Ramirez

Federal Reserve Banks - Federal Reserve Bank of Atlanta

Daniel F. Waggoner

Federal Reserve Bank of Atlanta

Multiple version iconThere are 2 versions of this paper

Date Written: 2018-11-05

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 is 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-Ramirez, Juan F. and Waggoner, Daniel F., Inference in Bayesian Proxy-Svars (2018-11-05). FRB of Philadelphia Working Paper No. 18-25. Available at SSRN: https://ssrn.com/abstract=3286208 or http://dx.doi.org/https://doi.org/10.21799/frbp.wp.2018.25

Jonas Arias (Contact Author)

Federal Reserve Bank of Philadelphia ( email )

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Philadelphia, PA 19106
United States

Juan F. Rubio-Ramirez

Federal Reserve Banks - Federal Reserve Bank of Atlanta

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
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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