期刊
JOURNAL OF ECONOMETRICS
卷 134, 期 2, 页码 553-577出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2005.07.005
关键词
advertising effectiveness; business cycles; EM algorithm; hidden Markov models; information criterion; Markov-switching regression
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 over-retention of states in the Markov chain, and it performs well in Monte Carlo studies with single and multiple states, small and large samples, and low and high noise. We illustrate the usefulness of MSC via applications to the U.S. business cycle and to media advertising. (c) 2005 Elsevier B.V. All rights reserved.
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