Unified inference for an integer-valued AR(1) model

Longyu Chen, Xiaohui Liu, Liang Peng and Fukang Zhu

Communications in Statistics - Theory and Methods, 2024, vol. 54, issue 12, 3732-3742

Abstract: Conditional least squares estimation is often employed to infer an integer-valued AR(1) model and its convergence rate and asymptotic variance differ for the stable and nearly unstable cases. This article adopts a random weighted bootstrap method to provide a unified interval estimation and hypothesis test regardless of the underlying process being either stable or nearly unstable. A simulation study confirms the good finite sample performance of the proposed inference. We also apply it to test for a unit root test in a COVID-19 dataset.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2403547 (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:2024:i:12:p:3732-3742

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

DOI: 10.1080/03610926.2024.2403547

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 ().