Econometric Methods for Nonlinear Panel Data Models

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9709598 Honore Panel data sets play an important role in empirical work in almost all areas oaf economics. In panel data, there is more than one observation for each cross sectional unit, and it is desirable to allow for an individual specific effect. For example, in the most typical panel data situation, an individual is observed in a number of different time periods. This project develops new econometric methods for dealing with non-linear panel data models. The types of models considered include models in which the variable of interest is "discrete." For example, one might be interested in investigating whether an individual buys a particular product in a given time period, or whether or not a firm is exporting. The project also considers models where the variable of interest is only partially observed ("censored"). For example, if one studies earnings using social security records, then the exact earnings are not known for individuals whose earnings are above the social security maximum. The methods developed in this project will relax some of the assumptions that must be maintained in order to apply existing methods. Specifically, for models with censoring, it was previously necessary to assume that the distribution of the unobserved error distribution is the same in all time periods. In many applications, this assumption is too restrictive. The project will also relax the assumptions that are made on the explanatory variables. In many applications, it is natural to allow for the possibility that explanatory variables in one time period are related to the value of the variable of interest. For example, a woman's decision to work may depend on whether or not she has a small child, which, in turn, may depend on whether the woman worked in previous time periods. To use existing methods, one must assume that such a "feedback" is not present. The methods developed by this project will allow for such a feedback. ??