Nonparametric Censored and Truncated Regression

This paper proposes new estimators of the latent regression function in nonparametric censored and truncated regression models. Our estimators are computationally convenient, consisting only of two nonparametric regressions and a univariate integral. We establish consistency and asymptotic normality for an implementation based on local linear kernel estimators. An extension permits estimation in the presence of a general form of heteroscedasticity

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Year of publication:	2002
Authors:	Lewbel, Arthur ; Linton, Oliver
Publisher:	[S.l.] : SSRN
Subject:	Theorie \| Theory \| Regressionsanalyse \| Regression analysis \| Nichtparametrisches Verfahren \| Nonparametric statistics
Description of contents:	Abstract [papers.ssrn.com]

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Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Econometrica, Vol. 70, pp. 765-779, 2002 Volltext nicht verfügbar
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014116321