Two-stage rank estimation of quantile index models

dc.contributor.author

Khan, S

dc.date.accessioned

2010-03-09T15:29:40Z

dc.date.issued

2001-02-01

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.mimetype

application/pdf

dc.identifier.issn

0304-4076

dc.identifier.uri

https://hdl.handle.net/10161/1917

dc.language.iso

en_US

dc.publisher

Elsevier BV

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

pubs.begin-page

319

pubs.end-page

355

pubs.issue

2

pubs.organisational-group

Duke

pubs.organisational-group

Economics

pubs.organisational-group

Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

100

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