A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form

79 Pages Posted: 21 Jul 2008

See all articles by Oliver B. Linton

Oliver B. Linton

University of Cambridge

Zhijie Xiao

University of Illinois at Urbana-Champaign - Department of Economics

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

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

Oliver B. Linton (Contact Author)

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Zhijie Xiao

University of Illinois at Urbana-Champaign - Department of Economics ( email )

410 David Kinley Hall
1407 W. Gregory
Urbana, IL 61801
United States
217-333-4520 (Phone)
217-244-6678 (Fax)

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