Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity

Abstract

In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage–Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations feasible and numerical standard errors small. As an empirical example, monetary models are compared ..