Mathematical Sciences: Robust Estimation and Testing of Econometric Models for Panel Data

数学科学：面板数据计量经济模型的稳健估计和测试

• 批准号: 9208820
• 负责人: Joel Horowitz
• 金额: $ 7.8万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-01 至 1995-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9208820&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Robust; Estimation; Testing

This award will support a collaborative effort in the areas of econometrics and statistics. In particular, the investigators will develop robust techniques for panel data. Panel data consist of a time series of observations on each of several individuals. Such data are used frequently in economics and certain biostatistical contexts. Standard methods for analysis of panel data are not robust to gross measurement errors in the data, and the results of analyses based on these methods can be highly sensitive to gross errors. This is well known to investigators who use panel data from sources such as the Current Population Survey. These investigators routinely use a variety of informal methods to "clean" the data before estimating models and testing hypotheses. The research proposed will proved formal methods for doing this. This award will support a collaborative effort in the areas of econometrics and statistics. In particular, the investigators will develop robust techniques for panel data. Panel data consist of a time series of observations on each of several individuals. Such data are used frequently in economics and certain biostatistical contexts.

该奖项将支持计量经济学和统计学领域的合作努力。 特别是，研究人员将为面板数据开发强大的技术。 面板数据由对几个个体中每个个体的观察的时间序列组成。 此类数据经常用于经济学和某些生物统计领域。 面板数据分析的标准方法对于数据中的粗测量误差并不稳健，并且基于这些方法的分析结果可能对粗误差高度敏感。 对于使用当前人口调查等来源的面板数据的调查人员来说，这是众所周知的。 这些研究人员在估计模型和检验假设之前通常会使用各种非正式方法来“清理”数据。 拟议的研究将证明实现这一目标的正式方法。 该奖项将支持计量经济学和统计学领域的合作努力。 特别是，研究人员将为面板数据开发强大的技术。 面板数据由对几个个体中每个个体的观察的时间序列组成。 此类数据经常用于经济学和某些生物统计领域。