Count network autoregression
Mirko Armillotta and
Konstantinos Fokianos
Journal of Time Series Analysis, 2024, vol. 45, issue 4, 584-612
Abstract:
We consider network autoregressive models for count data with a non‐random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi‐likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1111/jtsa.12728
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:bla:jtsera:v:45:y:2024:i:4:p:584-612
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782
Access Statistics for this article
Journal of Time Series Analysis is currently edited by M.B. Priestley
More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().