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

TL;DR

This paper introduces efficient Bayesian inference algorithms for SVARs with sign restrictions, using 'soft' restrictions and importance sampling to improve computational speed and robustness.

Contribution

It develops a novel sampling method combining 'soft' sign restrictions with importance sampling, enhancing efficiency and prior robustness in SVAR analysis.

Findings

Speeds up sampling in tightly identified models

Enables prior-robust Bayesian inference

Demonstrates applicability in oil-market model

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.