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dc.contributor.author Khan, S
dc.contributor.author Powell, JL
dc.date.accessioned 2010-03-09T15:29:32Z
dc.date.issued 2001-07-01
dc.identifier.citation Journal of Econometrics, 2001, 103 (1-2), pp. 73 - 110
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10161/1910
dc.description.abstract Root-n-consistent estimators of the regression coefficients in the linear censored regression model under conditional quantile restrictions on the error terms were proposed by Powell (Journal of Econometrics 25 (1984) 303-325, 32 (1986a) 143-155). While those estimators have desirable asymptotic properties under weak regularity conditions, simulation studies have shown these estimators to exhibit a small sample bias in the opposite direction of the least squares bias for censored data. This paper introduces two-step estimators for these models which minimize convex objective functions, and are designed to overcome this finite-sample bias. The paper gives regularity conditions under which the proposed two-step estimators are consistent and asymptotically normal; a Monte Carlo study compares the finite sample behavior of the proposed methods with their one-step counterparts. © 2001 Elsevier Science S.A. All rights reserved.
dc.format.extent 73 - 110
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Journal of Econometrics
dc.relation.isversionof 10.1016/S0304-4076(01)00040-9
dc.title Two-step estimation of semiparametric censored regression models
dc.type Journal Article
dc.department Economics
pubs.issue 1-2
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Economics
pubs.publication-status Published
pubs.volume 103

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