Time Series Modeling with Duration Dependent Markov-Switching Vector Autoregressions: MCMC Inference, Software and Applications

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, which switches according to a two-state Markov chain with transition probabilities depending on how long the process has been in a state. In the present paper I propose a MCMC-based methodology to carry out inference on the model's parameters and introduce DDMSVAR for Ox, a software written by the author for the analysis of time series by means of DDMS-VAR models. An application of the methodology to the U.S. business cycle concludes the article.Markov-switching, Business cycle, Gibbs sampling, Duration dependence, Vector autoregression