Two-step estimation of semiparametric censored regression models

Thumbnail Image



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats


Citation Stats


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.






Published Version (Please cite this version)


Publication Info

Khan, S, and JL Powell (2001). Two-step estimation of semiparametric censored regression models. Journal of Econometrics, 103(1-2). pp. 73–110. 10.1016/S0304-4076(01)00040-9 Retrieved from

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.