Nonparametric Least Squares Regression and Testing in E Conomic Models
نویسندگان
چکیده
This paper proposes a tractable and consistent estimator of the (possibly multi-equation) nonparametric regression model. The estimator is based on least squares over sets of functions bounded in Sobolev norm and is closely related to penalized least squares. We establish consistency and rate of convergence results as well as asymptotic normality of the (suitably standardized) sum of squared residuals is established. These results are then used to produce a -consistent, asymptotically normal estimator in the partial linear model . Conditional moment tests are provided for a variety of hypotheses including specification, significance, additive and multiplicative separability, monotonicity, concavity and demand theory. The validity of bootstrap test procedures is proved.
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