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A hidden Markov regime-switching smooth transition model

Author

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  • Elliott Robert J.

    (School of Commerce, University of South Australia, Australia; Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada)

  • Siu Tak Kuen

    (Department of Applied Finance and Actuarial Studies, Faculty of Business and Economics, Macquarie University, Sydney, Australia, Phone: (+61-2) 9850 8589; Fax: (+61-2) 9850 9481)

  • Lau John W.

    (Department of Mathematics and Statistics, University of Western Australia, Perth, Australia)

Abstract

In this paper, we develop a new class of parametric nonlinear time series models by combining two important classes of models, namely smooth transition models and hidden Markov regime-switching models. The class of models is general and flexible enough to incorporate two types of switching behavior: smooth state transitions and abrupt changes in hidden states. The estimation of the hidden states and model parameters is performed by applying filtering theory and a filter-based expectation-maximization (EM) algorithm. Applications of the model are illustrated using simulated data and real financial data. Other potential applications are mentioned.

Suggested Citation

  • Elliott Robert J. & Siu Tak Kuen & Lau John W., 2018. "A hidden Markov regime-switching smooth transition model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-21, September.
    • Handle: RePEc:bpj:sndecm:v:22:y:2018:i:4:p:21:n:2
    DOI: 10.1515/snde-2016-0061
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    References listed on IDEAS

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    Cited by:

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    2. De Gooijer, Jan G. & Henter, Gustav Eje & Yuan, Ao, 2022. "Kernel-based hidden Markov conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

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