Collabarative Research: Making Semiparametric Methods Operational: Bridging the Gap between Theory and Application

协作研究：使半参数方法可操作：弥合理论与应用之间的差距

• 批准号: 9730688
• 负责人: Hidehiko Ichimura
• 金额: $ 10.89万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2000-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9730688&HistoricalAwards=false
• 关键词: Collabarative; Research; Making; Semiparametric; Methods

9730688 Ichimura Much recent work in econometrics has been directed towards developing non-parametric and semi-parametric estimation approaches. These methods improve on traditional methods by allowing researchers to better uncover relationships in the data without having to make as many a priori modeling assumptions. They can also be used to test for the validity of parametric restrictions. Yet despite the potential broad applicability of these new estimation methods in empirical economic research, they are not widely used. This project addresses the reasons these methods are not used by: (1) resolving arbitrariness left in many semi-parametric procedures; (2) deriving general approximation results for data-dependent smoothing and trimming methods; (3) developing methods and software that overcome difficulties in computation with large data sets and (4) demonstrating how semi-parametric methods are useful for empirical work. The arbitrariness left in many semi-parametric procedures, which takes the form of parameters left unspecified, is addressed by developing effective, data-driven methods for choosing unspecified parameters. A preliminary Monte Carlo study comparing alternative methods demonstrates the effectiveness of the new methods. New asymptotic approximation results, needed to justify estimators that use data-dependent smoothing and trimming methods, are developed. Computational problems are resolved by adapting highly effective approximation techniques for use in estimating semi-parametric models. Two classes of empirical problems are shown to be promising areas to apply semi-parametric models: regression comparisons of two or more groups and the binary choice model. ??

小行星9730688 近年来，计量经济学的许多工作都是针对非参数和半参数估计方法。 这些方法改进了传统方法，使研究人员能够更好地揭示数据中的关系，而无需做出尽可能多的先验建模假设。 它们也可以用来测试参数限制的有效性。 然而，尽管这些新的估计方法在实证经济研究中具有潜在的广泛适用性，但它们并没有被广泛使用。 该项目解决了这些方法不被使用的原因：（1）解决许多半参数程序中留下的任意性;（2）为数据依赖的平滑和修剪方法导出一般近似结果;（3）开发克服大数据集计算困难的方法和软件;（4）演示半参数方法如何用于经验工作。 在许多半参数程序中，它采取的形式是未指定的参数留下的任意性，解决了开发有效的，数据驱动的方法来选择未指定的参数。 一个初步的蒙特卡罗研究比较替代方法证明了新方法的有效性。 新的渐近近似结果，需要证明估计，使用数据相关的平滑和修剪方法，开发。 计算问题的解决，采用高度有效的近似技术，用于估计半参数模型。 两类经验问题被证明是有前途的领域，应用半参数模型：两个或两个以上的群体和二元选择模型的回归比较。 ??