Estimation of Markov regime-switching regression models with endogenous switching
AbstractFollowing Hamilton (1989), estimation of Markov regime-switching regressions nearly always relies on the assumption that the latent state variable controlling the regime change is exogenous. We incorporate endogenous switching into a Markov-switching regression and develop strategies for identification and estimation. Identification requires instruments, which can be found in observed exogenous variables that influence the transition probabilities of the regime-switching process, as in the so-called time-varying transition probability case. However, even with fixed transition probabilities, the lagged state variable can serve as an instrument provided it is exogenous and the state process is serially dependent. This is true even though the lagged state is unobserved. A straightforward test for endogeneity is also presented. Monte Carlo experiments confirm that the estimation procedures perform quite well in practice. We apply the endogenous switching model to the volatility feedback model of equity returns given in Turner, Startz and Nelson (1989).
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Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2003-015.
Date of creation: 2004
Date of revision:
Other versions of this item:
- Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
- NEP-ECM-2003-09-24 (Econometrics)
- NEP-ETS-2003-09-24 (Econometric Time Series)
- NEP-EVO-2003-09-24 (Evolutionary Economics)
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