Quantile regression for panel data models with fixed effects under random censoring

The 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-pa...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods Vol. 49; no. 18; pp. 4430 - 4445
Main Authors: Xiaowen, Dai, Libin, Jin, Yuzhu, Tian, Maozai, Tian, Manlai, Tang
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 16.09.2020
Taylor & Francis Ltd
Subjects:
Computer simulation
Data models
Estimators
fixed effects
Kaplan-Meier estimator
Longitudinal studies
panel data
Quantile regression
random censoring
Regression
ISSN:0361-0926, 1532-415X
Online Access:Get full text
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Summary:The 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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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2019.1601221