Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically

Pedro Gozalo and Oliver Linton
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Pedro Gozalo: Brown University

No 1075, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We introduce a new kernel smoother for nonparametric regression that uses prior information on regression shape in the form of a parametric model. In effect, we nonparametrically encompass the parametric model. We derive pointwise and uniform consistency and the asymptotic distribution of our procedure. It has superior performance to the usual kernel estimators at or near the parametric model. It is particularly well motivated for binary data using the probit or logit parametric model as a base. We include an application to the Horowitz (1993) transport choice dataset.

Keywords: Kernel; nonparametric regression; parametric regression; binary choice (search for similar items in EconPapers)
JEL-codes: C4 C5 (search for similar items in EconPapers)
Pages: 41 pages
Date: 1994-08
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Citations: View citations in EconPapers (12)

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