Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/10747

Simulated Maximum Likelihood Estimation of Dynamic Discrete Choice Statistical Models Some Monte Carlo Results

Authors Lee, Lung Fei HKUST affiliated (currently or previously)
Issue Date 1998
Source Journal of Econometrics , v. 82, (1), 1998, JAN, p. 1-35
Summary This article reports Monte Carlo results on the simulated maximum likelihood estimation of discrete dynamic panel models introduced by James Heckman (1981a). The simulated maximum likelihood method is numerically stable even for long panels. Regression models and Polya and Renewal models can be better estimated than Markov models. With a moderate number of simulation draws, most of these complex models can be adequately estimated for panels with length up to 30. Polya and Renewal models can be accurately estimated for panels up to 50 periods. Estimates of Markov models can be sensitive to misspecified initial states but Polya, Renewal, and Habit Persistence models may not. (C) 1997 Elsevier Science S.A.
Subjects
ISSN 0304-4076
Language English
Format Article
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