Markov-switching models with endogenous explanatory variables II: A two-step MLE procedure

Chang Jin Kim

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

This paper proposes a two-step maximum likelihood estimation (MLE) procedure to deal with the problem of endogeneity in Markov-switching regression models. A joint estimation procedure provides us with an asymptotically most efficient estimator, but it is not always feasible, due to the 'curse of dimensionality' in the matrix of transition probabilities. A two-step estimation procedure, which ignores potential correlation between the latent state variables, suffers less from the 'curse of dimensionality', and it provides a reasonable alternative to the joint estimation procedure. In addition, our Monte Carlo experiments show that the two-step estimation procedure can be more efficient than the joint estimation procedure in finite samples, when there is zero or low correlation between the latent state variables.

Original languageEnglish
Pages (from-to)46-55
Number of pages10
JournalJournal of Econometrics
Volume148
Issue number1
DOIs
Publication statusPublished - 2009 Jan

Bibliographical note

Funding Information:
I thank the anonymous referees, associate editor, and Cheng Hsiao for thoughtful comments on the previous version of the paper. I acknowledge financial support from Korea Research Foundation Grant (KRF-2007-327-B00086) and from the Bryan C. Cressey Professorship at the University of Washington.

Keywords

  • Control function approach
  • Curse of dimensionality
  • Endogeneity
  • Markov switching
  • Smoothed probability
  • Two-step estimation procedure

ASJC Scopus subject areas

  • Economics and Econometrics

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