Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Zezhun Chen*, Angelos Dassios, George Tzougas

*Corresponding author for this work

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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.

Original languageEnglish
Pages (from-to)955-977
Number of pages23
JournalComputational Statistics
Issue number2
Early online date9 Jul 2022
Publication statusPublished - Jun 2023


  • Correlated time series
  • Count data time series
  • Maximum likelihood estimation
  • Multivariate INAR(1) regression models
  • Multivariate mixed Poisson-Generalized Inverse Gaussian

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics


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