Sequential estimation of censored quantile regression models

摘要

In this paper we propose sequential censored quantile regression (SCQR) and sequential instrumental variables censored quantile regression estimators (SIVCQR). We effectively transform the difficult censored quantile regression and censored instrumental variables quantile regression problems into more standard QR and IVQR procedures, consequently, our approaches make the quantile regression techniques for censored data easily accessible to applied researchers. Simulation results show that both estimators perform well. (C) 2018 Elsevier B.V. All rights reserved.