Research On Semiparametric and Nonparametric Estimation of Econometric Models

9307677霍洛维茨计量经济学最近的研究一直致力于开发半参数和非参数推理方法，以减少推理所需的先验假设的数量，并因此减少由于错误假设而导致不正确推理的可能性。该项目旨在以几种具有重要应用价值的方式扩展半参数方法和非参数方法。其中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自然减损的面板数据。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)因变量变换未知的回归模型的估计；(2)具有数据误差的回归模型的非参数估计；(3)磨损对从面板数据得出的推断的影响的非参数估计；(4)基于Bootstrap的临界值用于检验对广义矩方法(GMM)估计的过度识别限制；以及(5)带选择某些半参数和非参数估计。***

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. ***