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Quantile regression under random censoring

dc.contributor.author Honoré, B
dc.contributor.author Khan, S
dc.contributor.author Powell, JL
dc.date.accessioned 2010-03-09T15:29:14Z
dc.date.issued 2002-07-01
dc.identifier.issn 0304-4076
dc.identifier.uri https://hdl.handle.net/10161/1895
dc.description.abstract Censored regression models have received a great deal of attention in both the theoretical and applied econometric literature. Most of the existing estimation procedures for either cross-sectional or panel data models are designed only for models with fixed censoring. In this paper, a new procedure for adapting these estimators designed for fixed censoring to models with random censoring is proposed. This procedure is then applied to the CLAD and quantile estimators of Powell (J. Econom. 25 (1984) 303, 32 (1986a) 143) to obtain an estimator of the coefficients under a mild conditional quantile restriction on the error term that is applicable to samples exhibiting fixed or random censoring. The resulting estimator is shown to have desirable asymptotic properties, and performs well in a small-scale simulation study. © 2002 Elsevier Science B.V. All rights reserved.
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Elsevier BV
dc.relation.ispartof Journal of Econometrics
dc.relation.isversionof 10.1016/S0304-4076(01)00142-7
dc.title Quantile regression under random censoring
dc.type Journal article
duke.contributor.id Khan, S|0380552
pubs.begin-page 67
pubs.end-page 105
pubs.issue 1
pubs.organisational-group Duke
pubs.organisational-group Economics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 109


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