EconStor >
Humboldt-Universität zu Berlin >
Sonderforschungsbereich 373: Quantification and Simulation of Economic Processes, Humboldt-Universität Berlin >
Discussion Papers, SFB 373, HU Berlin >

Please use this identifier to cite or link to this item:
Title:A nonparametric regression estimator that adapts to error distribution of unknown form PDF Logo
Authors:Linton, Oliver Bruce
Xiao, Zhijie
Issue Date: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
Local Likelihood Estimation
Nonparametrie Regression
Persistent Identifier of the first edition:urn:nbn:de:kobv:11-10049681
Document Type:Working Paper
Appears in Collections:Discussion Papers, SFB 373, HU Berlin

Files in This Item:
File Description SizeFormat
724886826.pdf505.88 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.