Research In Econometric Theory

该项目的主要重点是面板数据，即包含每个人在一段时间内重复观察的数据。面板数据越来越多地成为劳工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随时间的变化，也能捕捉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信息细节。该项目为应用经济学家提供了统计工具，这些工具将在更好地利用面板数据中的信息方面发挥作用。本研究还扩展了估计行业技术效率变化的方法，改进了理性预期模型的预测方法。更具体地说，对面板数据的研究有三个组成部分。首先，项目考虑了动态面板数据模型，其中一个解释变量是因变量的滞后值。现有的这种模型的估计器是低效的，因为它们没有利用所有可用的矩条件，并且通过使用所有矩条件所获得的效率收益被量化。其次，考虑了各种具有自相关误差的面板数据模型。再一次，有可能找到利用所有可用矩条件的有效估计器，而有趣的问题是找到使用更少条件的同样有效的估计器；也就是说，找出哪些条件是多余的。第三，该项目研究了一个具有交互的个体和时间效应的模型。在广义矩方法和基于似然方法之间建立了联系，并将该模型推广到存在多个交互分量的情况。此外，该项目扩展了关于前沿生产功能的文献，允许随着时间的推移灵活地处理技术效率低下的模式。该项目调查了通过使用可观测的预测误差来估计理性预期模型的可能的效率收益。

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.