Research In Econometric Theory

这个项目的主要焦点是面板数据，即。，这些数据包含了每个人在一段时间内的重复观察。面板数据越来越成为劳动力和产业组织实证经济研究的首选数据，因为这些数据既能捕捉到随时间的变化，也能捕捉到给定时间点的横截面关系。现有的统计工具不允许经济学家利用面板数据提供的所有丰富的信息细节。这个项目为应用经济学家提供了统计工具，可以更好地利用面板数据中的信息。本研究也拓展了产业技术无效率变化的估计方法，改进了理性预期模型的预测方法。更具体地说，面板数据的研究有三个组成部分。首先，该项目考虑动态面板数据模型，其中一个解释变量是因变量的滞后值。该模型的现有估计器效率低下，因为它们不能利用所有可用的力矩条件，并且使用所有力矩条件所获得的效率收益是量化的。其次，考虑了各种具有自相关误差的面板数据模型。再一次，我们有可能找到利用所有可用矩条件的有效估计，有趣的问题是找到使用更少条件的同样有效的估计；也就是说，找出哪些条件是多余的。第三，研究了个体与时间相互作用的模型。将广义矩量法与基于似然的方法联系起来，并将模型推广到多个交互分量的情况下。此外，该项目扩展了前沿生产函数的文献，允许技术效率低下的灵活模式随着时间的推移。该项目研究了利用可观察到的预测误差对理性预期模型进行估计时可能获得的效率增益。

The principal focus of this project is panel data,i.e., data which contain repeated observations over time for each individual. Panel data are increasingly the data of choice for empirical economic research in labor and industrial organization because the data capture both changes over time and cross- sectional relationships at a given point of time. Existing statistical tools do not permit economists to utilize all of the rich informational detail provided by panel data. This project provides applied economists with statistical tools that will do as better job in exploiting the information in panel data. The research project also extends methods for estimating changes of industry technological inefficiency and it improves forecasting methods for rational expectations models. More specifically, the research on panel data has three components. First, the project considers the dynamic panel data model, in which one of the explanatory variables is the lagged value of the dependent variable. Existing estimators for this model are inefficient, because they fail to utilize all of the available moment conditions, and the efficiency gains from using all moment conditions are quantified. Second, various panel data models with autocorrelated errors are considered. Once again it is possible to find efficient estimators that make use of all available moment conditions, and the interesting question is to find equally efficient estimators that use less conditions; that is, to find out which conditions are superfluous. Third, the project investigates a model with interactive individual and time effects. A connection is made between generalized method of moments and likelihood-based approaches to this model, and the model is extended to the case of more than one interactive component. In addition, the project extends the literature on frontier production functions in ways that allow a flexible pattern of technical inefficiency over time. The project investigates the possible efficiency gains in estimation of rational expectations models from the use of observable forecast errors.