The paper shows that several estimators for the panel probit model suggested in the literature belong to a common class of GMM estimators. They are relatively easy to compute because they are based on conditional moment restrictions involving univariate moments of the binary dependent variable only. Applying nonparametric methods we discuss an estimator that is optimal in this class. A Monte Carlo study shows that a particular variant of this estimator has good small sample properties and that the efficiency loss compared to maximum likelihood is small. An application to the product innovation decisions of German firms reveals the expected efficiency gains.
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