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Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Author

Listed:
  • Zezhun Chen

    (London School of Economics)

  • Angelos Dassios

    (London School of Economics)

  • George Tzougas

    (Heriot-Watt University)

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.

Suggested Citation

  • Zezhun Chen & Angelos Dassios & George Tzougas, 2023. "Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression," Computational Statistics, Springer, vol. 38(2), pages 955-977, June.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01253-0
    DOI: 10.1007/s00180-022-01253-0
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    References listed on IDEAS

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