A large non-Gaussian structural VAR with application to Monetary Policy

TL;DR

This paper introduces a scalable large structural VAR model identified by higher moments, enabling richer economic analysis without restrictive assumptions, and demonstrates its effectiveness with real and artificial data.

Contribution

It develops a novel large structural VAR identified by higher moments, scalable to many variables, with an efficient estimation method and model comparison tools.

Findings

The model accurately estimates parameters with artificial data.

Including more variables improves structural analysis.

Prices and output respond with delays to monetary policy shocks.

Abstract

We propose a large structural VAR which is identified by higher moments without the need to impose economically motivated restrictions. The model scales well to higher dimensions, allowing the inclusion of a larger number of variables. We develop an efficient Gibbs sampler to estimate the model. We also present an estimator of the deviance information criterion to facilitate model comparison. Finally, we discuss how economically motivated restrictions can be added to the model. Experiments with artificial data show that the model possesses good estimation properties. Using real data we highlight the benefits of including more variables in the structural analysis. Specifically, we identify a monetary policy shock and provide empirical evidence that prices and economic output respond with a large delay to the monetary policy shock.