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MODELLING REGIME SWITCHING AND STRUCTURAL BREAKS WITH AN INFINITE HIDDEN MARKOV MODEL
Author(s) -
Song Yong
Publication year - 2014
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2337
Subject(s) - markov chain , computer science , parametric statistics , bayesian probability , econometrics , hidden markov model , markov chain monte carlo , parametric model , markov model , statistical physics , mathematics , artificial intelligence , machine learning , statistics , physics
SUMMARY This paper proposes an infinite hidden Markov model to integrate the regime switching and structural break dynamics in a unified 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 flexible approach allows for regime persistence and estimates the number of states automatically. An application to US real interest rates compares the new model to existing parametric alternatives. Copyright © 2013 John Wiley & Sons, Ltd.