Research in Econometric Theory

本研究涉及应用计量经济学中的三个重要课题，即面板数据的分析、时间序列数据的稳定性和工具变量的推导。面板数据，即包括时间序列和横断面观察的一组数据，已广泛用于研究消费者行为、进入和退出劳动力市场，以及家庭内部的决策。时间序列数据在分析一个经济体的长期表现和政府政策的影响方面起着非常重要的作用。当模型中需要的变量不是可观测的，或者具有干扰模型适用性的统计特性时，工具变量通常进入计量经济学研究。关于面板数据模型估计的工作将现有的线性模型的文献扩展到非线性模型。特别是，Logit和Probit模型被扩展到包括时变和随机参数。建立了具有滞后因变量的动态模型，并进行了分析。时间序列分析的研究涉及单位根检验的推导。检验包括允许变量中的自相关性。在关于趋势和平稳性的不同假设下，评估了检验的能力和稳健性。对工具变量估计量的研究为提高估计量的效率提供了一种简单实用的方法，并将三阶段最小二乘、全信息最大似然等不同估计量的处理统一在一个原则下。这个项目在理论计量经济学和应用计量经济学之间架起了一座重要的桥梁。它集中在计量经济学研究中广泛使用的三个重要主题，即面板数据的分析、时间序列的稳定性和工具变量的推导。术语“面板数据”指的是关于一组特定的个人、公司或组织随着时间的推移而观察到的任何数据集。在这方面，小组数据集既包括横截面数据(关于不同个人的)，也包括时间序列数据(在不同时间对同一组个人的观察)。这些数据被广泛用于劳动力市场、消费者行为和经济增长的经济分析。在总量经济研究中，时间序列数据是非常普遍的。该项目对给定的一组时间序列数据的稳定性进行测试，并在关于观测之间的相关性和整个序列随时间变化的趋势的各种假设下，检查这些测试的稳健性和能力。在经济模型中，工具变量经常被用来代替更相关的变量，以便给模型带来所需的统计特性。例如，在许多模型中，没有观察到相关变量，但可以观察到从它们派生的工具。这个项目提供了得出工具变量的有效方法，并将它们应用于几种估计技术。

This research addresses three important topics in applied econometrics, namely analysis of panel data, stability of time series data, and derivation of instrumental variables. Panel data, that is a set of data including both time series and cross sectional observations have become widely used in studies of consumer behavior, entrance into and exit from the labor market, and decision-making within the family. Time series data play a very important role in many analyses of the long term performance of an economy and the influence of government policy. Instrumental variables often come into econometric research when the variables required in a model are either not observable, or have statistical properties which interfere with the model's applicability. The work on estimation of models with panel data extends the existing literature on linear models to nonlinear models. In particular the logit and probit models are extended to include time varying and random parameters. Dynamic models with lagged dependent variables are also derived and analyzed. The research on time series analysis involves the derivation of tests for unit roots. The tests include allowance for autocorrelation in the variables. The power and robustness of the tests under varying assumptions concerning trend and stationarity are assessed. The work on instrumental variable estimators provides a simple and easily applicable method of improving the efficiency of estimators, and brings under one general principle the treatments of diverse estimators such as 3-Stage Least Squares and Full Information Maximum Likelihood. %%% This project bridges an important gap between theoretical and applied econometrics. It focuses on three important topics widely used in econometric research, namely analysis of panel data, stability of time series, and the derivation of instrumental variables. The term "panel data" refers to any data set of observations on a given group of individuals, firms, or organizations taken over time. In this respect a panel data set is comprised of both cross sectional data (on the different individuals) and time series data (observations on the same group of individuals taken at different times). Such data are widely used in economic analysis of the labor market, consumer behavior, and economic growth. Time series data are very prevalent in studies of the aggregate economy. This project provides tests for the stability of a given set of time series data, and examines the robustness and power of those tests under various assumptions about the correlation among the observations and the tendency of the entire series to change through time. Instrumental variables are often used in economic models in place of more relevant variables in order to bring desired statistical properties to the model. For instance, in many models the relevant variables are not observed, but instruments derived from them can be observed. This project provides efficient methods for deriving instrumental variables and applies them to several estimation techniques.