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A transitional Markov switching autoregressive model

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  • J. Cheng

Abstract

This paper is concerned with properties of a transitional Markov switching autoregressive (TMSAR) model, together with its maximum-likelihood estimation and inference. We extend existing MSAR models by allowing dependence of AR parameters on hidden states at time points prior to the current time t. A stationary solution is given and expressions for the theoretical autocovariance function are derived. Two time series are analyzed and the new model outperforms two existing MSAR models in terms of maximized log-likelihood, residual correlations, and one-step-ahead forecasting performance. The new model also gives more regime changes in agreement with real events.

Suggested Citation

  • J. Cheng, 2016. "A transitional Markov switching autoregressive model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(10), pages 2785-2800, May.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:10:p:2785-2800
    DOI: 10.1080/03610926.2014.894065
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    Cited by:

    1. Thanh, Su Dinh & Canh, Nguyen Phuc & Maiti, Moinak, 2020. "Asymmetric effects of unanticipated monetary shocks on stock prices: Emerging market evidence," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 40-55.

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