Quantile regression under random censoring

Bo Honoré, Shakeeb Khan, James L. Powell

Research output: Contribution to journalArticle

68 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)67-105
Number of pages39
JournalJournal of Econometrics
Volume109
Issue number1
DOIs
StatePublished - Jul 1 2002

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Accelerated failure time model
  • Censored quantile regression
  • Kaplan-Meier product limit estimator
  • Random censoring

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