Inference in Bayesian Proxy-SVARs

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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), this study develops an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. The algorithm allows independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. The approach enables researchers to simultaneously utilize proxies and traditional constraints to identify structural shocks, and demonstrates its methods through two applications.