We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

This paper examines maximum likelihood estimation via hill climbing and the expectations maximization (EM) algorithm in the context of Hamilton's Markov switching framework. The techniques are explained in detail and are followed by a discussion of both analytic and computational issues. Both...