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Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Author

Listed:
  • Chen, Zezhun
  • Dassios, Angelos
  • Tzougas, George

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

  • Chen, Zezhun & Dassios, Angelos & Tzougas, George, 2022. "Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression," LSE Research Online Documents on Economics 115369, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:115369
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    File URL: http://eprints.lse.ac.uk/115369/
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    References listed on IDEAS

    as
    1. Abdallah, Anas & Boucher, Jean-Philippe & Cossette, Hélène, 2016. "Sarmanov family of multivariate distributions for bivariate dynamic claim counts model," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 120-133.
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    More about this item

    Keywords

    count data time series; multivariate INAR(1) regression models; multivariate mixed Poisson- Generalized Inverse Gaussian; correlated time series; maximum likelihood estimation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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