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Markov-Switching Model Selection Using Kullback-Leibler Divergence
Social Science Research Network
In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. Specifically, we derive a new information criterion, Markov switching criterion (MSC), which is an estimate of KL divergence. MSC imposes an appropriate penalty to mitigate the overretention of states in the Markov chain, and it performs well in Monte Carlo studies with single and multiple states, small and largedoi:10.2139/ssrn.711404 fatcat:syn5exdstffmlmhwpklrfjawlu