Research in Econometric Theory

计量经济理论研究

• 批准号: 8821154
• 负责人: Peter Schmidt
• 金额: $ 11.58万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-04-01 至 1992-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8821154&HistoricalAwards=false
• 关键词: Research; Econometric; Theory

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.

本研究涉及应用计量经济学中的三个重要主题， 即面板数据分析、时间序列数据的稳定性，以及 工具变量的推导。 面板数据，即一组 包括时间序列和横截面观察的数据， 被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。 时间序列数据在许多分析中起着非常重要的作用， 经济的长期表现和政府的影响 政策 计量经济学研究中经常会涉及到工具变量 当模型中所需的变量不可观察时，或 具有干扰模型的统计特性， 适用性 面板数据模型估计的工作扩展了现有的 从线性模型到非线性模型。 特别是 logit和probit模型被扩展以包括时变和 随机参数 具有滞后因变量的动态模型是 并进行了推导和分析。 时间序列分析研究 涉及到单位根检验的推导。 述测试包括 允许变量的自相关性。 电力和 在有关趋势和 稳定性进行了评估。 关于工具变量的工作 估计器提供了一种简单且易于应用的方法， 估计的效率，并带来了一个一般原则 不同估计量的处理，如3-阶段最小二乘法和 完整信息最大可能性 %%% 该项目弥合了理论和应用之间的重要差距 计量经济学 它集中在三个重要的主题广泛使用， 计量经济学研究，即面板数据分析，时间稳定性 系列，以及工具变量的推导。 术语“面板数据”是指对一组数据的任何观测数据集。 随着时间的推移，一组特定的个人、公司或组织。 在这方面，面板数据集由两个交叉 截面数据（不同个体）和时间序列数据 （对同一组人在不同地点的观察结果） times）。 这些数据被广泛用于经济分析， 劳动力市场、消费者行为和经济增长。 时间序列数据是研究聚集体的常用数据 经济 这个项目提供了一个给定集合的稳定性测试 的时间序列数据，并检查这些数据的鲁棒性和力量， 在各种假设下进行测试， 观察结果和整个系列的变化趋势， 时间 在经济模型中，工具变量经常被用来代替 更多的相关变量，以使所需的统计 属性到模型。 例如，在许多模型中， 变量不被观察到，但从它们衍生的工具可以被 观察 该项目提供了有效的方法， 工具变量，并将其应用于几个估计 技术.