Estimation of Markov regime-switching regression models with endogenous switching

Chang Jin Kim, Jeremy Piger, Richard Startz

Research output: Contribution to journalArticlepeer-review

137 Citations (Scopus)


Following Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357-384], estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive filters. The model nests the exogenous switching model, yielding straightforward tests for endogeneity. In Monte Carlo experiments, maximum likelihood estimates of the endogenous switching model parameters were quite accurate, even in the presence of certain model misspecifications. As an application, we extend the volatility feedback model of equity returns given in Turner et al. [1989. A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics 25, 3-22] to allow for endogenous switching.

Original languageEnglish
Pages (from-to)263-273
Number of pages11
JournalJournal of Econometrics
Issue number2
Publication statusPublished - 2008 Apr


  • Endogeneity
  • Regime-switching

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'Estimation of Markov regime-switching regression models with endogenous switching'. Together they form a unique fingerprint.

Cite this