Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities

Abstract

This paper proposes a model which allows for discrete stochastic breaks in the timevarying transition probabilities of Markov-switching models with autoregressive dynamics. An extensive simulation study is undertaken to examine the properties of the maximum-likelihood estimator and related statistics, and to investigate the implications of misspecification due to unaccounted changes in the parameters of the Markov transition mechanism. An empirical application that examines the relationship between Argentinian sovereign bond spreads and output growth is also discussed.