Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Zezhun Chen (), Angelos Dassios and George Tzougas
Additional contact information
Zezhun Chen: London School of Economics
Angelos Dassios: London School of Economics
George Tzougas: Heriot-Watt University

Computational Statistics, 2023, vol. 38, issue 2, No 16, 955-977

Abstract: Abstract In this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint distribution of innovations across all the sequences. Finally, for illustrative purposes different members of the MMPGIG-INAR(1) class are fitted to Local Government Property Insurance Fund data from the state of Wisconsin via maximum likelihood estimation.

Keywords: Count data time series; Multivariate INAR(1) regression models; Multivariate mixed Poisson-Generalized Inverse Gaussian; Correlated time series; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01253-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01253-0

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01253-0

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().