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

Oliver Linton and Zhijie Xiao

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

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)
Date: 2001-06
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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)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:419

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