Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results

Abstract

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.