SUBSAMPLING INFERENCE FOR NONPARAMETRIC EXTREMAL CONDITIONAL QUANTILES

Daisuke Kurisu and Taisuke Otsu

Econometric Theory, 2025, vol. 41, issue 2, 326-340

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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:41:y:2025:i:2:p:326-340_3

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