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Subsampling inference for nonparametric extremal conditional quantiles

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  • Kurisu, Daisuke
  • Otsu, Taisuke

Abstract

This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance parameters in the limiting distribution of the local estimator. A simulation study and empirical example illustrate usefulness of our subsampling inference to investigate extremal phenomena.

Suggested Citation

  • Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:120365
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    File URL: http://eprints.lse.ac.uk/120365/
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    References listed on IDEAS

    as
    1. Huixia Judy Wang & Deyuan Li & Xuming He, 2012. "Estimation of High Conditional Quantiles for Heavy-Tailed Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1453-1464, December.
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    5. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.
    6. Daouia, Abdelaati & Gardes, Laurent & Girard, Stephane, 2011. "On kernel smoothing for extremal quantile regression," LIDAM Discussion Papers ISBA 2011031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    8. repec:hal:wpspec:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    9. Bashtannyk, David M. & Hyndman, Rob J., 2001. "Bandwidth selection for kernel conditional density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
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    More about this item

    Keywords

    quantile regression; subsampling; extreme value theory;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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