Document Type
Discussion Paper
Publication Date
8-1-1994
CFDP Number
1075
CFDP Revision Date
1997-12-01
CFDP Pages
41
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.
Recommended Citation
Gozalo, Pedro and Linton, Oliver B., "Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically" (1994). Cowles Foundation Discussion Papers. 1318.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/1318