Estimation of the Duration Model by Nonparametric Maximum Likelihood, Maximum Penalized Likelihood, and Probability Simulators

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Abstract
Failure to properly treat heterogeneity components in longitudinal analyses can result in an incorrect parameterization of the duration model. Estimation bias is not limited to duration dependence but also extends to the structural parameters. Our paper uses Monte Carlo methods to examine the finite sample behavior of three estimators for this problem: nonparametric maximum likelihood, maximum penalized likelihood, and the probability simulator. Our results on the estimators' finite sample behavior for this class of model add to limited experimental evidence. They highlight the estimators' computational feasibility and point to their relative strengths in empirical duration modeling.

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