A NONPARAMETRIC REGRESSION ESTIMATOR THAT ADAPTS TO ERROR DISTRIBUTION OF UNKNOWN FORM

Oliver Bruce Linton () and Zhijie Xiao

Econometric Theory, 2007, vol. 23, issue 03, pages 371-413

Abstract: We propose a new kernel estimator for nonparametric regression with unknown error distribution. We show that the proposed estimator is adaptive in the sense that it is asymptotically equivalent to the infeasible local likelihood estimator (Staniswalis, 1989, Journal of the American Statistical Association 84, 276 283; Fan, Farmen, and Gijbels, 1998, Journal of the Royal Statistical Society, Series B 60, 591 608; and Fan and Chen, 1999, Journal of the Royal Statistical Society, Series B 61, 927 943), which requires knowledge of the error distribution. Hence, our estimator improves on standard nonparametric kernel estimators when the error distribution is not normal. A Monte Carlo experiment is conducted to investigate the finite-sample performance of our procedure.We thank Yuichi Kitamura, Yanqin Fan, Joel Horowitz, Roger Koenker, Jens Perch Nielsen, Peter Phillips, Peter Robinson, Tom Rothenberg, and two referees for helpful comments. Financial support from the NSF and the ESRC (UK) is gratefully acknowledged.

Date: 2007

Downloads: (external link)
http://journals.cambridge.org/abstract_S026646660707017X link to article abstract page (text/html)

Related works:
Working Paper: A Nonparametric Regression Estimator that Adapts to Error Distribution of unknown Form
Working Paper: A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:cup:etheor:v:23:y:2007:i:03:p:371-413_07

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press
Address: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Series data maintained by Mike Eden ().