Working Paper

Inference Based On Time-Varying SVARs Identified with Time Restrictions

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.

Keywords: time-varying parameters; structural vector autoregressions; identification;

JEL Classification: C11; C51; E52; E58;

Status: Published in 2024

Access Documents


Bibliographic Information

Provider: Federal Reserve Bank of Atlanta

Part of Series: FRB Atlanta Working Paper

Publication Date: 2024-03-25

Number: 2024-4