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Inference Based on Time-Varying SVARs Identified with Sign Restrictions

Jonas E. Arias¹; Juan F. Rubio-Ramírez^2,3; Minchul Shin¹; Daniel F. Waggoner^2,4

¹ Federal Reserve Bank of Philadelphia · ² Economics Department, Emory University , Atlanta , · ³ Federal Reserve Bank of Atlanta · ⁴ Federal Reserve Bank of Atlanta, Emeritus

Abstract We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge.

DOI

10.1093/restud/rdag008

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en

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