A nonparametric regression estimator that adapts to error distribution of unknown form

Oliver Linton and Zhijie Xiao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: Adaptive estimation; asymptotic expansions; efficiency; kernel; local likelihood estimation; nonparametric regression (search for similar items in EconPapers)
JEL-codes: C13 C14 C24 (search for similar items in EconPapers)
Pages: 84 pages
Date: 2001-06
References: Add references at CitEc
Citations:

Downloads: (external link)
http://eprints.lse.ac.uk/2120/ Open access version. (application/pdf)

Related works:
Journal Article: A NONPARAMETRIC REGRESSION ESTIMATOR THAT ADAPTS TO ERROR DISTRIBUTION OF UNKNOWN FORM (2007)
Working Paper: A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form (2001)
Working Paper: A nonparametric regression estimator that adapts to error distribution of unknown form (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2120

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().