Research On Semiparametric and Nonparametric Estimation of Econometric Models

小行星9307677 计量经济学最近的许多研究都关注于发展半参数和非参数的推断方法，以减少推断所需的先验假设的数量，从而减少由于错误假设而导致的不正确推断的可能性。 该项目旨在以几种在应用中具有重要意义的方式扩展半参数和非参数方法。 这些方法包括转换模型的半参数和非参数估计、数据中有误差的模型和有损耗的面板数据。 将开发具有重要经济应用的新方法。 该项目在以下领域开发了新的方法和经济应用：（1）估计因变量变换未知的回归模型;（2）对数据中有误差的回归模型进行非参数估计;（3）非参数估计自然减员对从面板数据得出的推论的影响;（4）对非参数估计的影响。（4）使用基于Bootstrap的临界值来检验广义矩法（GMM）估计的过度识别限制;（5）对某些半参数和非参数估计量的带宽度选择。 ***

9307677 Horowitz Much recent research in econometrics has been concerned with developing semi- and nonparametric methods of inference in order to reduce the number of a priori and, often, untestable assumptions needed for inference and, thereby, reduce the potential for incorrect inference due to erroneous assumptions. The project aims at extending semi- and nonparametric methods in several ways that have important in applications. These include the semi- and nonparametric estimation of transformation models, models with errors in the data, and panel data with attrition. New methods will be developed that have important economic applications. The project developed new methods and economic applications in the following areas: (1) Estimation of a regression model with an unknown transformation of the dependent variable; (2) Nonparametric estimation of regression models with errors in the data; (3) Nonparametric estimation of the effects of attrition on inferences derived from panel data; (4) Use of bootstrap-based critical values for the test of overidentifying restrictions on generalized-method-of moment (GMM) estimation; and (5) Bandwith selection for certain semiparametric and nonparmetric estimators. ***