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dc.contributor.author Khan, S
dc.date.accessioned 2010-03-09T15:29:40Z
dc.date.issued 2001-02-01
dc.identifier.citation Journal of Econometrics, 2001, 100 (2), pp. 319 - 355
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10161/1917
dc.description.abstract This paper estimates a class of models which satisfy a monotonicity condition on the conditional quantile function of the response variable. This class includes as a special case the monotonic transformation model with the error term satisfying a conditional quantile restriction, thus allowing for very general forms of conditional heteroscedasticity. A two-stage approach is adopted to estimate the relevant parameters. In the first stage the conditional quantile function is estimated nonparametrically by the local polynomial estimator discussed in Chaudhuri (Journal of Multivariate Analysis 39 (1991a) 246-269; Annals of Statistics 19 (1991b) 760-777) and Cavanagh (1996, Preprint). In the second stage, the monotonicity of the quantile function is exploited to estimate the parameters of interest by maximizing a rank-based objective function. The proposed estimator is shown to have desirable asymptotic properties and can then also be used for dimensionality reduction or to estimate the unknown structural function in the context of a transformation model. © 2001 Elsevier Science S.A. All rights reserved.
dc.format.extent 319 - 355
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Journal of Econometrics
dc.relation.isversionof 10.1016/S0304-4076(00)00040-3
dc.title Two-stage rank estimation of quantile index models
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.publication-status Published
pubs.volume 100

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