Inference in Bayesian Proxy-SVARs

Jonas Arias,
Juan F. Rubio-Ramírez &
Daniel F. Waggoner

October 2020

WP 18-25R — 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.

A previous version of this working paper was originally published in November 2018.

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.

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