Investigating Some Issues Relating to Regime Matching

Markov switching models are a common tool used in many disciplines as well as in Economics, and estimation methods are available in many software packages. Estimated models are commonly used for allocating observations to regimes. This allocation is usually done using a rule based on the estimated smoothed probabilities, such as, in the two regime case, when it exceeds the threshold of 0.5. The accuracy of the regime matching is often measured by the concordance index. Can regime matching be improved by using other rules? By replicating a number of published two-and three- regime studies and the use of simulation methods, it demonstrates that other rules can improve on the performance of the rule based on the threshold of 0.5. Using simulated models we extend the analysis of a single series to investigate, and demonstrate the efficacy of Markov switching models identifying a common factor in multiple time series.