A Bayesian regime-switching time-series model

Abstract

This article provides a new Bayesian approach for AR(2) time-series models with multiple regime-switching points. Our formulation of the regime-switching model involves a binary discrete variable that indicates the regime change. This variable is specified to be detected by data in each regime. The model is estimated using Stochastic approximation Monte Carlo method proposed by Liang et al. [JASA (2007)]. This methodology is quite useful since it allows for fitting of more complex regime-switching models without transition constraint. The proposed model is illustrated using simulated and real data such as GNP and US interest rate data.