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

Linton, Oliver and Xiao, Zhijie (2001) A nonparametric regression estimator that adapts to error distribution of unknown form. Econometrics; EM/2001/419, EM/01/419. Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science, London, UK.

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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.

Item Type:	Monograph (Discussion Paper)
Official URL:	http://sticerd.lse.ac.uk
Additional Information:	© 2001 the authors
Uncontrolled Keywords:	Adaptive estimation; asymptotic expansions; efficiency; kernel; local likelihood estimation; nonparametric regression
Library of Congress subject classification:	H Social Sciences > HB Economic Theory
Journal of Economic Literature Classification System:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C24 - Truncated and Censored Models
Sets:	Research centres and groups > Financial Markets Group (FMG) Collections > Economists Online Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD) Departments > Economics
Rights:	http://www.lse.ac.uk/library/rights/LSERO.htm
Identification Number:	EM/01/419
URL:	http://eprints.lse.ac.uk/2120/

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