Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators

By W. Arulampalam, Mark B. Stewart. Social Science Research Network, 2009

212 citations recorded by the source corpus.

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases all three estimators display satisfactory performance, except when the number of time periods is very small.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/5f7b158ca2b1ab387d83a77c90da71421b69f46c

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In Economics and Econometrics

Citations in the corpus

Avoiding biased versions of Wooldridge’s simple solution to the initial conditions problem (2013, methodology): “As shown in Table 2, Wooldridge’s model W performs well, as was also found by Arulampalam and Stewart (2009), with estimated relative bias and RMSE similar to model P.”