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

Chang-Jin Kim ()

Journal of Econometrics, 2009, vol. 148, issue 1, 46-55

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.

Keywords: Control; function; approach; Curse; of; dimensionality; Endogeneity; Markov; switching; Two-step; estimation; procedure; Smoothed; probability (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:148:y:2009:i:1:p:46-55

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