Quantile regression under random censoring

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of econometrics 2002-07, Vol.109 (1), p.67-105
Main Authors: Honoré, Bo, Khan, Shakeeb, Powell, James L.
Format: Article
Language:English
Subjects:
Accelerated failure time model
Censored quantile regression
Econometrics
Economic models
Economics
Exact sciences and technology
Kaplan–Meier product limit estimator
Linear inference, regression
Mathematics
Probability and statistics
Random censoring
Regression analysis
Sciences and techniques of general use
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0304-4076
1872-6895
DOI:10.1016/S0304-4076(01)00142-7