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Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown

Author

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  • Bruno Damásio
  • João Nicolau

Abstract

Markov chains models are used in several applications and different areas of study. Usually a Markov chain model is assumed to be homogeneous in the sense that the transition probabilities are time invariant. Yet, ignoring the inhomogeneous nature of a stochastic process by disregarding the presence of structural breaks can lead to misleading conclusions. Several methodologies are currently proposed for detecting structural breaks in a Markov chain, however, these methods have some limitations, namely they can only test directly for the presence of a single structural break. This paper proposes a new methodology for detecting and testing the presence multiple structural breaks in a Markov chain occurring at unknown dates.

Suggested Citation

  • Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp01362020
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    References listed on IDEAS

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    Keywords

    Inhomogeneous Markov chain; structural breaks; time-varying probabilities;
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