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A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs

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  • Jonas E. Arias
  • Juan F. Rubio-Ram'irez
  • Minchul Shin

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 apart 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.

Suggested Citation

  • Jonas E. Arias & Juan F. Rubio-Ram'irez & Minchul Shin, 2025. "A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs," Papers 2505.23542, arXiv.org, revised May 2025.
    • Handle: RePEc:arx:papers:2505.23542
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