Jonas E. Arias, Juan F. Rubio-Ramírez, Minchul Shin, and Daniel F. Waggoner
Working Paper 2024-4
March 2024
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.
JEL classification: C11, C51, E52, E58
Key words: time-varying parameters, structural vector autoregressions, identification
https://doi.org/10.29338/wp2024-04
The authors thank Mark Bognanni, Frank Schorfheide, Christian Wolf, and Jonathan Wright for helpful comments. 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.
Please address questions regarding content to Juan Rubio-Ramírez (corresponding author), Emory University, Economics Department, Emory University, Rich Memorial Building, Room 306, Atlanta, GA 30322-2240.
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