A transitional Markov switching autoregressive model

J. Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2785-2800
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number10
Publication statusPublished - 18 May 2016


  • Autocovariance structure
  • Filter and smoothed probabilities
  • Markov switching autoregressive models
  • Stationary time series


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