Estimation of Markov regime-switching regression models with endogenous switching
AbstractFollowing 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 143 (2008)
Issue (Month): 2 (April)
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Other versions of this item:
- Chang-Jin Kim & Jeremy M. Piger & Richard Startz, 2004. "Estimation of Markov regime-switching regression models with endogenous switching," Working Papers 2003-015, Federal Reserve Bank of St. Louis.
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