Nonparametric inference for extremal conditional quantiles

Daisuke Kurisu and Taisuke Otsu

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: This paper studies asymptotic properties of the local linear quantile estimator under the extremal order quantile asymptotics, and develops a practical inference method for conditional quantiles in extreme tail areas. By using a point process technique, the asymptotic distribution of the local linear quantile estimator is derived as a minimizer of certain functional of a Poisson point process that involves nuisance parameters. To circumvent difficulty of estimating those nuisance parameters, we propose a subsampling inference method for conditional extreme quantiles based on a self-normalized version of the local linear estimator. A simulation study illustrates usefulness of our subsampling inference to investigate extremal phenomena.

Keywords: Quantile regression; Extreme value theory; Point process; Subsampling (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2021-09
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://sticerd.lse.ac.uk/dps/em/em616.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:616

Access Statistics for this paper

More papers in STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
Bibliographic data for series maintained by ().