Econometric Methods for Nonlinear Panel Data Models

非线性面板数据模型的计量经济学方法

• 批准号: 9709598
• 负责人: Bo Honore
• 金额: $ 20.45万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9709598&HistoricalAwards=false
• 关键词: Econometric; Methods; Nonlinear; Panel; Data

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. ??

小行星9709598 面板数据集在经济学的几乎所有领域的实证研究中都扮演着重要的角色. 在面板数据中，每个横截面单元有一个以上的观察结果，并且需要考虑到个体的特定效应。 例如，在最典型的面板数据情况下，在许多不同的时间段内观察个体。 该项目开发用于处理非线性面板数据模型的新计量经济学方法。 所考虑的模型类型包括感兴趣的变量为“离散”的模型。“例如，人们可能感兴趣的是调查一个人是否在某一时期购买了某种产品，或者一家公司是否在出口。 该项目还考虑了模型中的感兴趣的变量只是部分观察（“审查”）。 例如，如果使用社会保障记录研究收入，则收入高于社会保障最高限额的个人的确切收入是未知的。 在这个项目中开发的方法将放宽一些必须保持的假设，以应用现有的方法。具体来说，对于删失模型，以前需要假设未观察到的误差分布在所有时间段内都是相同的。 在许多应用中，这种假设过于严格。 该项目还将放宽对解释变量所作的假设。 在许多应用中，考虑到一个时间段内的解释变量与感兴趣的变量的值相关的可能性是很自然的。 例如，妇女是否工作的决定可能取决于她是否有小孩，而小孩又可能取决于妇女以前是否工作过。 为了使用现有的方法，必须假设不存在这样的“反馈”。 该项目开发的方法将允许这样的反馈。 ??