Inference in Bayesian Proxy-SVARs

Jonas E. Arias, Juan F. Rubio-Ramírez, and Daniel F. Waggoner
Working Paper 2018-16a
December 2018 (Revised January 2021)

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Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify structural shocks. We illustrate our methods with two applications. In particular, we show how to generalize the counterfactual analysis in Mertens and Montiel-Olea (2018) to identified structural shocks.

JEL classification: C15, C32

Key words: SVARs, external instruments, importance sampler

The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of Philadelphia, or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. No statements here should be treated as legal advice. Philadelphia Fed working papers are free to downloadOff-site link.
Please address questions regarding content to Jonas E. Arias, Federal Reserve Bank of Philadelphia; Juan F. Rubio-Ramírez, Emory University/Federal Reserve Bank of Atlanta; or Daniel F. Waggoner, Federal Reserve Bank of Atlanta, .
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