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Multivariate Generalizations of the Markov-Switching Model

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  • Mohamad Khaled

    (University of Paris I Pantheon-Sorbonne)

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    Abstract

    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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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 297.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:297

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    Related research

    Keywords: Bayesian statistics; markov-switching; matrix-variate bernoulli distribution; multivariate generalizations.;

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