Nonparametric and Semiparametric Inference in Econometric Models

该项目开发了非参数和半参数方法， 计量经济模型的估计。 本研究主要包括三个方面 部分：1）加性交互回归，2）协方差矩阵 异方差和自相关估计 未知形式的一般推理方法; 3）未知形式的一般推理方法 半参数模型 加性交互回归（AIR）模型是一种计量经济模型 这使回归函数比非参数回归函数更有结构性 模型，从而规避维度问题， 严重阻碍了完全非参数技术在实际应用中的应用 计量经济学 该项目通过分析 AIR模型的统计特性，并开发有效的 估计技术对他们来说。 变参数下协方差矩阵的估计 分布假设扩展了现有的工作比较 通过确定合适的带宽/滞后， 截断参数与这些估计，并比较 这些估计量与其他协方差估计量的性质 估计器。 此外，计算效率高的估计 技术被开发用于获得一致性反对任意 异方差的形式。 %%% 本项目研究各种估计的性质 计量经济学模型中使用的技术。 这些模型被广泛应用于 应用经济学的所有领域，包括宏观经济学和 政府政策分析，企业行为研究， 产业和国际经济的动态。 直到 最近，所有这些类型的计量经济学研究都是由 假设一个数学函数或函数系统模拟 经济关系有待考察。 研究表明，在 在许多情况下，这些研究的结果可能取决于 函数的特定数学规范。 在过去的十年里，许多计量经济学工作都集中在发展 统计估计技术，不需要特定的 数学函数 这些技术一般称为 非参数技术，有明显的优势， 非参数的，在较少的假设结构， 经济必须被制造出来，更多关于经济的信息是 来源于数据本身。 然而，统计问题出现了。 使用非参数技术。 该项目增加了 显着的非参数估计的现有工作， 研究三个最困难的问题，即维度， 协方差估计和计算效率。

This project develops nonparametric and semiparametric methods for estimation of econometric models. The research consists of three main parts: 1) additive interactive regression, 2) covariance matrix estimation in the presence of heteroskedasticity and autocorrelation of unknown forms, and 3) general methods for inference in semiparametric models. Additive Interactive Regression (AIR) models are econometric models which put more structure on the regression function than nonparametric models, and thus circumvent the problems of dimensionality that severely hinder the use of fully nonparametric techniques in applied econometrics. This project adds to that line of work by analyzing the statistical properties of AIR models, and developing efficient estimation techniques for them. The work on estimation of the covariance matric under varying distributional assumptions extends existing work on comparing different kernel estimators by determining suitable bandwidth/lag truncation parameters for use with these estimators, and comparing the properties of these estimators with those of other covariance estimators. In addition, computationally efficient estimation techniques are developed for obtaining consistency against arbitrary forms of heteroskedasticity. %%% This project investigates the properties of various estimation techniques used in econometric models. Such models are used widely in all areas of applied economics, including macroeconomics and government policy analysis, studies of the behavior of firms and industries, and the dynamics of the international economy. Until recently, all these types of econometric investigations started by postulating a mathematical function or system of functions simulating the economic relationships to be examined. Research has shown that in many cases the results of these studies can be dependent on the particular mathematical specification of the functions. During the last decade much econometric work has focused on developing statistical estimation techniques which do not require a specified mathematical function. These techniques, known generically as nonparametric techniques, have obvious advantages over the nonparametric ones in that fewer assumptions about the structure of the economy have to be made, and more information about the economy is derived from the data themselves. However, statistical problems arise with the use of nonparametric techniques. This project adds significantly to the existing work on nonparametric estimation by examining three of the most difficult problems, namely dimensionality, covariance estimation, and computational efficiency.