Weighted quantile regression for censored data with application to export duration data

Xiaofeng Lv (), Gupeng Zhang, Xinkuo Xu and Qinghai Li
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Xiaofeng Lv: Southwestern University of Finance and Economics
Gupeng Zhang: University of Chinese Academy of Science
Xinkuo Xu: Capital University of Economics and Business
Qinghai Li: Nanjing University of Finance and Economics

Statistical Papers, 2019, vol. 60, issue 4, No 8, 1192 pages

Abstract: Abstract Existing literature on censored quantile regression requires global linearity, bandwidth selection, or complex computation. In the current study, we propose weighted quantile regression for censored data with weights obtained through Aalen’s estimator. Our estimator is simple to compute and does not require bandwidth selection and global linearity. It can be applied to unconditionally and conditionally independent censoring even if the censoring depends on the error terms conditional on covariates. The proposed estimator is consistent and asymptotically normal. We illustrate the finite-sample performance of our estimator through simulations. Finally, we apply our method to the export duration data of China’s agricultural products. Empirical results show that the effects of determinants on duration vary across quantiles.

Keywords: Censored quantile regression; Aalen estimator; Export duration (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00362-016-0868-2

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