Econometric Methods for Nonlinear Panel Data Models

9709598荣誉面板数据集在几乎所有经济学领域的实证工作中都发挥着重要作用。在面板数据中，每个横截面单位有一个以上的观测，并且需要考虑到个别的特定效应。例如，在最典型的面板数据情况下，可以在多个不同的时间段观察到一个人。该项目开发了处理非线性面板数据模型的新计量经济学方法。考虑的模型类型包括感兴趣的变量是“离散的”的模型。例如，人们可能有兴趣调查个人是否在给定的时间段内购买了特定的产品，或者公司是否正在出口。该项目还考虑了感兴趣的变量只被部分观察(“审查”)的模型。例如，如果一个人使用社会保障记录来研究收入，那么收入超过社会保障上限的个人的确切收入是未知的。本项目中开发的方法将放宽为应用现有方法而必须保持的一些假设。具体地说，对于有删失的模型，以前需要假设未观测到的误差分布在所有时间段中的分布是相同的。在许多应用中，这一假设过于严格。该项目还将放宽对解释变量的假设。在许多应用中，允许一个时间段内的解释变量与感兴趣变量的值相关的可能性是很自然的。例如，一名妇女的工作决定可能取决于她是否有年幼的孩子，而这又可能取决于该妇女是否在以前的时间段工作。要使用现有的方法，必须假定不存在这样的“反馈”。该项目开发的方法将允许这样的反馈。?？

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. ??