Quantile regression under random censoring
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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 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.
Published Version (Please cite this version)10.1016/S0304-4076(01)00142-7
Publication InfoHonoré, B; Khan, S; & Powell, JL (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.
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Professor of Economics
Professor Khan is on leave at Boston College for the 2016-17 academic year.Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects ent