Skip to main content

Research Repository

Advanced Search

A transitional Markov switching autoregressive model




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.


Cheng. (2016). A transitional Markov switching autoregressive model. Communications in Statistics - Theory and Methods, 45(10), 2785-2800.

Journal Article Type Article
Acceptance Date Feb 10, 2014
Online Publication Date Apr 18, 2016
Publication Date 2016
Journal Communications in Statistics - Theory and Methods
Print ISSN 0361-0926
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 45
Issue 10
Pages 2785-2800
Keywords autocovariance structure; filter and smoothed probabilities; Markov switching autoregressive models; Stationary time series
Publisher URL