Theoretical and practical issues in estimation of nonlinear panel data models

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。在形式上，面板数据集是其中每个观察单位有一个以上观察的数据集。通常，这是因为数据集包含对同一个人在一段时间内的观察，但处理面板数据的方法同样适用于观察单位为公司、市政当局等的情况。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，包括持续时间模型。该项目还开发了实现这些方法的软件。面板数据的优势在于，通过跟踪一个人一段时间，有可能调查一个人的动态行为，同时控制该人的特征。通过考虑下面的简单示例，可以最容易地说明这一点的重要性。假设，就像通常的情况一样，人们观察到，在一个时期使用某个社会计划的个人，在下一个时期也更有可能使用它。这种“持久性”可以被观察到，因为个人的某些特征使他或她更有可能使用程序。如果这一特征不会随着时间的推移发生太大变化，那么相同的人将倾向于在不同的时间段使用该程序。另一方面，持久性也与使用程序会导致个人将来更有可能使用程序的情况一致(例如，因为个人通过使用程序来了解程序)。能够区分这些对持续性的解释是很重要的，因为在第二种解释下，改变计划在单个时期的可用性的政策也将对未来时期参与计划产生间接影响。这个项目开发了计量经济学工具，用于处理像这样的情况下的面板数据，尽管这些工具更一般，因此也适用于许多其他经验情况。

Panel data sets play an important role in empirical work in almost all areas of economics. Formally, panel data sets are data sets where there is more than one observation for each unit of observation. Typically, this is because the data set consists of observations for the same individual over a period of time, but the methods for dealing with panel data are equally applicable to situations where the unit of observation is firms, municipalities, etc. This project develops new econometric methods for dealing with nonlinear panel data models, including duration models. The project also develops software to implement these methods. The advantage of panel data is that by following an individual over time, it is possible to investigate an individual's dynamic behavior while controlling for that individual's characteristics. It is easiest to illustrate the importance of this by considering the following simple example. Suppose, as is often the case, that one observed that individuals, who use a certain social program in one period, are also more likely to use it the next period. This ``persistence'' could be observed because some characteristic of the individual makes him or her more likely to use the program. If that characteristic does not change much over time, then the same people will tend to use the program in different time periods. On the other hand, the persistence is also consistent with a situation in which using the program causes the individual to be more likely to use the program in the future (for example, because the individual learns about the program by using it). Being able to distinguish between these explanations of the persistence is important because under the second explanation, a policy that changes the availability of the program in a single period will also have an indirect effect on participation in the program in future periods. This project develops econometric tools for dealing with panel data in situations like this one, although the tools are more generic and hence applicable in many other empirical situations as well.