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.
View the Full Working Paper
Working Paper
A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs
May 2025
WP 25-19 – We develop a new algorithm to analyze economic data using SVAR models identified with sign restrictions. We demonstrate its usefulness on a small SVAR of the world oil market and a large SVAR of the U.S. economy.