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

In this presentation, Juan Rubio-Ramírez introduces a novel inference algorithm for structural vector autoregressions (SVARs) identified by sign restrictions. The key aspect of the algorithm is to move beyond the traditional accept–reject tradition associated with sign-identified SVARs. He demonstrates how embedding an elliptical slice sampling within a Gibbs sampler approach can deliver dramatic gains in speed and turn previously infeasible applications into feasible ones. A tractable example will illustrate the power of elliptical slice sampling for sign-identified SVARs. Finally, Juan will demonstrate the algorithm’s utility by applying it to a well-known small-SVAR model of the oil market featuring a tight identified set, and to a large-SVAR model with more than 100 sign restrictions.