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dc.contributor.author Chen, S
dc.contributor.author Khan, S
dc.date.accessioned 2010-03-09T15:29:52Z
dc.date.issued 2000
dc.identifier.citation Journal of Econometrics, 2000, 98 (2), pp. 283 - 316
dc.identifier.uri http://hdl.handle.net/10161/1919
dc.identifier.uri http://dx.doi.org/10.1016/S0304-4076(00)00020-8
dc.description.abstract Powell's (1984, Journal of Econometrics 25, 303-325) censored least absolute deviations (CLAD) estimator for the censored linear regression model has been regarded as a desirable alternative to maximum likelihood estimation methods due to its robustness to conditional heteroskedasticity and distributional mis specification of the error term. However, the CLAD estimation procedure has failed in certain empirical applications due to the restrictive nature of the 'full rank' condition it requires. This condition can be especially problematic when the data are heavily censored. In this paper we introduce estimation procedures for heteroskedastic censored linear regression models with a much weaker identification restriction than that required for the LCAD, and which are flexible enough to allow for various degrees of censoring. The new estimators are shown to have desirable asymptotic properties and perform well in small-scale simulation studies, and can thus be considered as viable alternatives for estimating censored regression models, especially for applications in which the CLAD fails. © 2000 Elsevier Science S.A. All rights reserved.
dc.format.extent 283 - 316
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Journal of Econometrics
dc.title Estimating censored regression models in the presence of nonparametric multiplicative heteroskedasticity
dc.type Journal Article
dc.department Economics
pubs.issue 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.volume 98

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