Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62744
Authors: 
Linton, Oliver Bruce
Xiao, Zhijie
Year of Publication: 
2001
Series/Report no.: 
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2001,33
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.
Subjects: 
Adaptive Estimation
Asymptotic Expansions
Efficiency
Kernel
Local Likelihood Estimation
Nonparametrie Regression
JEL: 
C13
C14
C24
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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