Asymptotic normality of kernel density estimation for mixing high-frequency data

Shanchao Yang, Lanjiao Qin, Y. Wang and X. Yang

Journal of Nonparametric Statistics, 2024, vol. 36, issue 4, 1151-1176

Abstract: High-frequency data is widely used and studied in many fields. In this paper, the asymptotic normality of kernel density estimator under ρ-mixing high-frequency data is studied. We first derive some moment inequalities for mixing high-frequency data, and then use them to study the asymptotic normality of the kernel density estimator, and give Berry-Esseen upper bounds. The numerical simulations report that the kernel density estimation of high-frequency data has asymptotic normality, and the result is consistent with the theoretical conclusions. The actual data analysis shows that the kernel density estimation can well capture the characteristics of the distribution, and can use these features and the least square deviation principle to fit the parameter model, which is more convenient for further theoretical analysis and application analysis.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2024.2307393 (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:gnstxx:v:36:y:2024:i:4:p:1151-1176

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2024.2307393

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().