WP 26-04/R – 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.
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A previous version of this working paper was originally published in January 2026, superseding Working Paper 25-19 – A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs
We develop a new algorithm for inference in structural vector autoregressions (SVARs) identified with sign restrictions that can accommodate big data and modern identification schemes. The key innovation of our approach is to move beyond the traditional accept–reject framework commonly used in sign-identified SVARs. We show that an elliptical slice within Gibbs sampler can deliver dramatic gains in computational speed and render previously infeasible applications tractable. We also prove that the algorithm is well defined, in the sense that its stationary distribution coincides with the posterior distribution of interest. To illustrate the approach in the context of sign-identified SVARs, we use a tractable example. We further assess the performance of our algorithm through two applications: a well-known small-SVAR model of the oil market featuring a tight identified set and a large SVAR model with more than 10 shocks and 100 sign restrictions.
