Quantile regression for panel data models with fixed effects under random censoring |
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| Authors: | Dai Xiaowen Jin Libin Tian Yuzhu Tian Maozai |
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| Institution: | 1. School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China;2. School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China;3. School of Statistics, Renmin University of China, Beijing, China;4. School of Statistics, Lanzhou University of Finance and Economics, Lanzhou, China |
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| Abstract: | AbstractThe locally weighted censored quantile regression approach is proposed for panel data models with fixed effects, which allows for random censoring. The resulting estimators are obtained by employing the fixed effects quantile regression method. The weights are selected either parametrically, semi-parametrically or non-parametrically. The large panel data asymptotics are used in an attempt to cope with the incidental parameter problem. The consistency and limiting distribution of the proposed estimator are also derived. The finite sample performance of the proposed estimators are examined via Monte Carlo simulations. |
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| Keywords: | Quantile regression panel data fixed effects random censoring Kaplan-Meier estimator |
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