We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behavior of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.
# 14-072/III (2014-06-17)
- Marco Bazzi, University of Padova, Italy; Francisco Blasques, VU University Amsterdam; Siem Jan Koopman, VU University Amsterdam; Andre Lucas, VU University Amsterdam, the Netherlands
- Hidden Markov Models; observation driven models; generalized autoregressive score dynamics
- JEL codes:
- C22, C32