Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions

Matthew Read and Dan Zhu

Papers from arXiv.org

Abstract: We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially speeds up sampling when identification is tight. It also facilitates implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in an oil-market model identified using a rich set of sign, elasticity and narrative restrictions.

Date: 2026-03
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Working Paper: Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions (2025)
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