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Multivariate Generalizations of the Markov-Switching Model

Author

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  • Mohamad Khaled

    (University of Paris I Pantheon-Sorbonne)

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

Suggested Citation

  • Mohamad Khaled, 2006. "Multivariate Generalizations of the Markov-Switching Model," Computing in Economics and Finance 2006 297, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:297
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    More about this item

    Keywords

    Bayesian statistics; markov-switching; matrix-variate bernoulli distribution; multivariate generalizations.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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