Date of this Version
Journal of Time Series Analysis
We show that the covariance function of a second-order stationary vector Markov regime switching time series has a vector ARMA(p,q) representation, where upper bounds for p and q are elementary functions of the number of regimes. These bounds apply to vector Markov regime switching processes with both mean–variance and autoregressive switching. This result yields an easily computed method for setting a lower bound on the number of underlying Markov regimes from an estimated autocovariance function.
This is the peer reviewed version of the following article: Zhang, J. and Stine, R. A. (2001), Autocovariance Structure of Markov Regime Switching Models and Model Selection. Journal of Time Series Analysis, 22: 107–124., which has been published in final form at doi: 10.1111/1467-9892.00214. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms.
ARMA, AIC, BIC
Zhang, J., & Stine, R. A. (2001). Autocovariance Structure of Markov Regime Switching Models and Model Selection. Journal of Time Series Analysis, 22 (1), 107-124. http://dx.doi.org/10.1111/1467-9892.00214
Date Posted: 27 November 2017
This document has been peer reviewed.