Time-Varying Identification of Structural Vector Autoregressions

TL;DR

This paper introduces a Bayesian Markov-switching SVAR model that dynamically identifies structural shocks using time-varying volatility and data-driven exclusion restrictions, improving understanding of monetary policy shocks.

Contribution

It develops a novel Bayesian framework combining heteroskedasticity and Markov-switching to allow data-driven, time-varying identification of structural shocks in SVAR models.

Findings

Data supports time variation in US monetary policy shock identification.

Time-varying volatility helps identify monetary policy shocks within regimes.

Model enables over-identification of restrictions driven by data signals.

Abstract

We propose a novel Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects among alternative patterns of exclusion restrictions to identify structural shocks within the Markov process regimes. We implement the selection through a multinomial prior distribution over these patterns, which is a spike'n'slab prior for individual parameters. By combining a Markov-switching structural matrix with heteroskedastic structural shocks following a stochastic volatility process, the model enables shock identification through time-varying volatility within a regime. As a result, the exclusion restrictions become over-identifying, and their selection is driven by the signal from the data. Our empirical application shows that data support time variation in the US monetary policy shock identification. We also verify that time-varying volatility identifies the monetary policy shock within the regimes.