Least squares estimation for discretely observed Ornstein–Uhlenbeck process driven by small stable noises

Chao Wei

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 12, 4138-4150

Abstract: This article is concerned with the drift parameter estimation for Ornstein–Uhlenbeck process driven by small α-stable noises. The contrast function is given to obtain the least squares estimators and the error of estimation are obtained. The consistency, the rate of convergence and asymptotic distribution of estimators are derived when a small dispersion coefficient ε→0 and n→∞ simultaneously. Some numerical calculus and simulations are made to verify the effectiveness of the estimators.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1986537 (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:52:y:2023:i:12:p:4138-4150

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

DOI: 10.1080/03610926.2021.1986537

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 ().