A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form
79 Pages Posted: 21 Jul 2008
Date Written: June 2001
Abstract
We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum likelihood estimator [Staniswalis (1989)], and hence improves on standard kernel estimators when the error distribution is not normal. We investigate the finite sample performance of our procedure on simulated data.
JEL Classification: C13, C14
Suggested Citation: Suggested Citation
Linton, Oliver B. and Xiao, Zhijie, A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form (June 2001). LSE STICERD Research Paper No. EM419, Available at SSRN: https://ssrn.com/abstract=1162600
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