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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2019. "Weighted quantile regression for censored data with application to export duration data," Statistical Papers, Springer, vol. 60(4), pages 1161-1192, August.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:4:d:10.1007_s00362-016-0868-2
    DOI: 10.1007/s00362-016-0868-2
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    References listed on IDEAS

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    Cited by:

    1. Luis Felipe Beltrán Morales, 2022. "Impact of the COVID-19 Pandemic on Export Survival from Latin American Countries," Sustainability, MDPI, vol. 14(14), pages 1-16, July.

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