A transitional Markov switching autoregressive model

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.

Citation

Cheng. (2016). A transitional Markov switching autoregressive model. Communications in Statistics - Theory and Methods, 45(10), 2785-2800. https://doi.org/10.1080/03610926.2014.894065

• 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
• DOI: https://doi.org/10.1080/03610926.2014.894065
• Keywords: autocovariance structure; filter and smoothed probabilities; Markov switching autoregressive models; Stationary time series
• Publisher URL: https://doi.org/10.1080/03610926.2014.894065