Modelling Regime Switching and Structural Breaks with an Infinite Dimension Markov Switching Model
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.
|Date of creation:||15 Apr 2011|
|Date of revision:|
|Contact details of provider:|| Postal: 150 St. George Street, Toronto, Ontario|
Phone: (416) 978-5283
When requesting a correction, please mention this item's handle: RePEc:tor:tecipa:tecipa-427. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RePEc Maintainer)
If references are entirely missing, you can add them using this form.