Estimation of tobit-type models with individual specific effects

The aim of this paper is two-fold. First, we review recent estimators for censored regression and sample selection panel data models with unobservable individual specific effects, and show how the idea behind these estimators can be used to construct estimators for a variety of other Tobit-type models. The estimators presented in this paper are semiparametric, in the sense that they do not require the parametrization of the distribution of the unobservables. The second aim of the paper is to introduce a new class of estimators for the censored regression model. The advantage of the new estimators is that they can be applied under a stationarity assumption on the transitory error terms, which is weaker than the exchangeability assumption that is usually made in this literature. A similar generalization does not seem feasible for the estimators of the other models that are considered.

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