Working Paper
A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs
Abstract: We develop a new algorithm for inference based on structural vector autoregressions (SVARs) identified with sign restrictions. The key insight of our algorithm is to break from the accept-reject tradition associated with sign-identified SVARs. We show that embedding an elliptical slice sampling within a Gibbs sampler approach can deliver dramatic gains in speed and turn previously infeasible applications into feasible ones. We provide a tractable example to illustrate the power of the elliptical slice sampling applied to sign-identified SVARs. We demonstrate the usefulness of our algorithm by applying it to a well-known small SVAR model of the oil market featuring a tight identified set, as well as to a large SVAR model with more than 100 sign restrictions.
JEL Classification: C32;
https://doi.org/10.21799/frbp.wp.2025.19
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Bibliographic Information
Provider: Federal Reserve Bank of Philadelphia
Part of Series: Working Papers
Publication Date: 2025-05-30
Number: 25-19