Tail single-index regression with locally stationary regressors

Tao Xu, Yu Chen and Hongfang Sun

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 20, 6652-6669

Abstract: In extreme value theory, the tail index parameter controls the tail behavior of a distribution function and is thus of primary interest in analyzing extreme events. Recent developments in modeling the tail index along with covariates have been in semi-parametric regression, but there is a lack of flexible models for time series data, especially for non stationary data. To handle such cases, this article proposes a novel tail single-index regression model incorporating locally stationary covariates to address time-varying tail behaviors. For the proposed model, we develop an estimation procedure by proposing an iterative algorithm and a selection method for the tuning parameter. The asymptotic properties of the estimators are constructed in the time-dependent context. Numerical studies and an analysis of Ozone data demonstrate the effectiveness of our model and corresponding theories.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2461608 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:54:y:2025:i:20:p:6652-6669

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2025.2461608

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().