Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models

Abstract

We present a new method for imposing parameter restrictions in Markov-Switching Vector Autoregression (MS-VAR) models. Our method is more flexible than competing methodologies and easily handles a range of parameter restrictions over different equations, regimes and parameter types. We also expand the range of priors used in the MS-VAR literature. We demonstrate the versatility of our approach using three appropriate examples.

Keywords: Parameter Restrictions, MS-VAR estimation, Block Exogeneity, Zero Restrictions, Bayesian estimation