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

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.

@article{arias2025_2505.23542,
  title={ A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs },
  author={ Jonas E. Arias and Juan F. Rubio-Ramírez and Minchul Shin },
  journal={arXiv preprint arXiv:2505.23542},
  year={ 2025 }
}