Inference in Tightly Identified and Large-Scale Sign-Restricted SVARs

TL;DR

This paper introduces a novel reparameterization method for inference in large-scale, tightly identified SVARs that incorporates inequality restrictions and improves computational efficiency.

Contribution

It presents a new approach using reparameterization and Hamiltonian Monte Carlo to handle inequality restrictions in high-dimensional SVARs more effectively.

Findings

Lower serial dependence in Markov chains

Larger effective sample sizes

Reduced computation time

Abstract

We propose a new approach to inference in tightly identified and large-scale structural vector autoregressions based on a reparameterization that enables imposing identifying inequality restrictions through continuously differentiable mappings. Permitted inequality restrictions include shape and ranking restrictions as well as bounds on economically relevant elasticities, and the approach is also able to accommodate zero restrictions in a straightforward manner. We implement a Hamiltonian Monte Carlo algorithm and show how the posterior density can be rapidly evaluated under the reparameterization, thus facilitating inference in high-dimensional settings. Two empirical applications demonstrate that our approach tends to result in lower serial dependence in Markov chains, larger effective sample sizes and reduced computation time relative to existing methods.