Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/300458 
Year of Publication: 
2024
Series/Report no.: 
Working Paper No. 2024-4
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
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.
Subjects: 
time-varying parameters
structural vector autoregressions
identification
JEL: 
C11
C51
E52
E58
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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