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

9813492佩特拉计量经济学最近的许多工作都是针对发展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发现数据中的关系，而不必做出许多先验的建模假设。它们也可以用来检验参数限制的有效性。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，但它们并未得到广泛应用。本项目解决了这些方法未被使用的原因：(1)解决许多半参数过程中留下的任意性；(2)推导出与数据相关的平滑和修剪方法的一般近似结果；(3)开发克服大型数据集计算困难的方法和软件；(4)证明半参数方法对经验工作的有用性。在许多半参数过程中留下的任意性，采取未指定参数的形式，是通过开发有效的，数据驱动的方法来选择未指定参数来解决的。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。新的渐近逼近结果，需要证明估计使用数据相关的平滑和修剪方法，开发。计算问题是通过采用高效的近似技术来解决的，用于估计半参数模型。两类经验问题被证明是有希望应用半参数模型的领域：两个或更多组的回归比较和二元选择模型。

9813492 Petra 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.