Abstract: This paper proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a
Bayesian framework. Two parallel hierarchical structures, one governing the transition
probabilities and another governing the parameters of the conditional data density,
keep the model parsimonious and improve forecasts. This nonparametric approach
allows for regime persistence and estimates the number of states automatically. A
global identification algorithm for structural changes versus regime switching is
presented. Applications to U.S. real interest rates and inflation compare the new model
to existing parametric alternatives. Besides identifying episodes of regime switching
and structural breaks, the hierarchical distribution governing the parameters of the
conditional data density provides significant gains to forecasting precision.
Keywords: hidden Markov model; Bayesian nonparametrics; Dirchlet process
JEL Classification: C51; C53; C22; C11