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

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Author Info
Oliver Linton
Zhijie Xiao

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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.

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Publisher Info
Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2001/419.

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Date of creation: Jun 2001
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Handle: RePEc:cep:stiecm:/2001/419

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Related research
Keywords: Adaptive estimation; asymptotic expansions; efficiency; kernel; local likelihood estimation; nonparametric regression.;

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  1. Thanasis Stengos & Ximing Wu, 2005. "Partially Adaptive Estimation via Maximum Entropy Densities," University of Cyprus Working Papers in Economics 6-2005, University of Cyprus Department of Economics. [Downloadable!]
  2. Thanasis Stengos & Yiguo Sun, 2005. "The Absolute Health Income Hypothesis Revisited : A Semiparametric Quantile Regression Approach," University of Cyprus Working Papers in Economics 7-2005, University of Cyprus Department of Economics. [Downloadable!]
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