Research On Semiparametric and Nonparametric Estimation of Econometric Models

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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. ***