Markov-Switching Model Selection Using Kullback-Leibler Divergence

Aaron D. Smith, Prasad A. Naik, Chih-Ling Tsai
2005 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 large
more » ... , and low and high noise. We illustrate the usefulness of MSC via applications to the U.S. business cycle and to media advertising. r 2005 Elsevier B.V. All rights reserved. JEL classification: C22; C52.
doi:10.2139/ssrn.711404 fatcat:syn5exdstffmlmhwpklrfjawlu