Duration Dependent Markov-Switching Vector Autoregression: Properties, Bayesian Inference, Software and Application

Abstract

Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes. The switching between the two VAR processes is governed by a two state Markov chain with transition probabilities that depend on how long the chain has been in a state. In the present paper we analyze the second order properties of such models and propose a Markov chain Monte Carlo algorithm to carry out Bayesian inference on the model's unknowns. Furthermore, a freeware software written by the author for the analysis of time series by means of DDMS-VAR models is illustrated. The methodology and the software are applied to the analysis of the U.S. business cycle.

Keywords: Markov-switching, business cycle, Gibbs sampler, duration dependence, vector autoregression

JEL Classification: C11, C13, C15, C32, C41, C51, C87, E32