Multivariate Generalizations of the Markov-Switching Model
We present a multivariate generalization of the simple markov-switching model. We allow for the introduction of several latent processes that have a simple parametric distribution. The matrix-variate bernoulli distribution yields a flexible yet parsimonious pattern of dependence between the different latent processes while preserving the markovian property. We also show how to estimate the model in the bayesian framework and give several examples
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