Markov-switching models with endogenous explanatory variables

Chang Jin Kim

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


The maximum likelihood estimation of a Markov-switching regression model based on the Hamilton filter is not valid in the presence of endogenous explanatory variables. However, we show that there exists an appropriate transformation of the model that allows us to directly employ the Hamilton filter. The transformed model explicitly allows for a vector of bias correction terms as additional regressors, and the new disturbance term is uncorrelated with all the regressors in the transformed model. Within this framework, a quasi maximum likelihood estimation procedure is presented. A procedure to test for endogeneity based on the Wald statistic or the likelihood ratio statistic is also presented.

Original languageEnglish
Pages (from-to)127-136
Number of pages10
JournalJournal of Econometrics
Issue number1
Publication statusPublished - 2004 Sept

Bibliographical note

Funding Information:
This work has been supported by Korea Research Foundation Grant (KRF-2002-041-B00053). I thank Yunmi Kim for excellent research assistence.


  • Bias correction
  • Endogeneity
  • Forward-looking monetary policy rule
  • Hausman-Wu test
  • Markov switching

ASJC Scopus subject areas

  • Economics and Econometrics


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