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

9813492佩特拉最近在计量经济学中的许多工作都指向开发非参数和半参数估计方法。这些方法对传统方法进行了改进，允许研究人员更好地发现数据中的关系，而不必做出尽可能多的先验建模假设。它们还可用于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。这个项目解决了这些方法没有被使用的原因：(1)解决了许多半参数程序中留下的任意性；(2)推导了依赖于数据的平滑和修剪方法的一般近似结果；(3)开发了克服大数据集计算困难的方法和软件；(4)演示了半参数方法如何对经验工作有用。通过开发有效的、数据驱动的方法来选择未指定的参数，解决了许多半参数程序中留下的任意性，这些半参数程序的形式为未指定的参数。蒙特卡罗初步研究比较了各种方法，证明了新方法的有效性。发展了新的渐近逼近结果，以证明使用数据相关平滑和修剪方法的估计量是正确的。通过采用高效的近似技术来解决计算问题，以用于估计半参数模型。两类经验问题被认为是应用半参数模型的有前景的领域：两组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.