Quantile regression under random censoring

Loading...
Thumbnail Image

Date

2002-07-01

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

300
views
1232
downloads

Citation Stats

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.

Department

Description

Provenance

Subjects

Citation

Published Version (Please cite this version)

10.1016/S0304-4076(01)00142-7

Publication Info

Honoré, B, S Khan and JL Powell (2002). Quantile regression under random censoring. Journal of Econometrics, 109(1). pp. 67–105. 10.1016/S0304-4076(01)00142-7 Retrieved from https://hdl.handle.net/10161/1895.

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.