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

Oliver Linton and Zhijie Xiao

No 2001,33, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

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; Nonparametrie Regression (search for similar items in EconPapers)
JEL-codes: C13 C14 C24 (search for similar items in EconPapers)
Date: 2001
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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:zbw:sfb373:200133

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