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Federal Reserve Bank of Philadelphia
Working Papers
Inference in Bayesian Proxy-SVARs
Jonas E. Arias
Juan F. Rubio-Ramirez
Daniel F. Waggoner
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).


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Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, Inference in Bayesian Proxy-SVARs, Federal Reserve Bank of Philadelphia, Working Papers 18-25, 05 Nov 2018.
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Keywords: SVARs; External Instruments; Importance Sampler
DOI: https://doi.org/10.21799/frbp.wp.2018.25
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