Subsampling inference for nonparametric extremal conditional quantiles

Kurisu, Daisuke and Otsu, Taisuke ORCID: 0000-0002-2307-143X (2023) Subsampling inference for nonparametric extremal conditional quantiles. Econometric Theory. ISSN 0266-4666

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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.

Item Type:	Article
Official URL:	https://www.cambridge.org/core/journals/econometri...
Additional Information:	© 2023 The Author(s)
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
Date Deposited:	04 Oct 2023 14:57
Last Modified:	31 Oct 2025 19:45
URI:	http://eprints.lse.ac.uk/id/eprint/120365

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