Quantile Regression under Random Censoring


Honoré, Bo E., Shakeeb Khan, and James L. Powell. 2002. “Quantile Regression under Random Censoring.” Journal of Econometrics 109 (1): 67-105.

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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 arc 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 (1984, 1986) 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.

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Last updated on 12/28/2018