Bayesian Analysis of Markov Switching Vector Error Correction Model

This paper introduces a Bayesian approach to a Markov switching vector error correction model that allows for regime shifts in the intercept terms, the lag terms, the adjustment terms and the variance-covariance matrix. The proposed Bayesian method allows for estimation of the cointegrating vector within a nonlinear framework through Gibbs sampling so that it generates more efficient estimation than classical approaches that require a multi-stage maximum likelihood procedure. The Bayes factors are applied to test for Markov switching and model specifications. We apply the proposed model to U.S. term structure of interest rates allowing the risk premium and other parameters in the model to change with regime.Bayesian inference, Nonlinear cointegration, Markov switching model, Gibbs sampling, Bayes factor

Bayesian analysis of a vector autoregressive model with multiple structural breaks

This paper develops a Bayesian approach for analyzing a vector autoregressive model with multiple structural breaks based on MCMC simulation methods, extending a method developed for the univariate case by Wang and Zivot (2000). It derives the conditional posterior densities using an independent Normal-Wishart prior. The number of structural breaks is chosen by the posterior model probability based on the marginal likelihood, calculated here by the method of Chib (1995) rather than the Gelfand-Dey (1994) method used by Wang and Zivot. Monte Carlo simulations demonstrate that the approach provides generally accurate estimation for the number of structural breaks as well as their locations.Bayesian inference Structural break Cointegration Bayes factor