IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v8y2025i1p22-d1610824.html
   My bibliography  Save this article

A Flexible Bivariate Integer-Valued Autoregressive of Order (1) Model for Over- and Under-Dispersed Time Series Applications

Author

Listed:
  • Naushad Mamode Khan

    (Department of Economics and Statistics, University of Mauritius, Reduit 80835, Mauritius
    These authors contributed equally to this work.)

  • Yuvraj Sunecher

    (Department of Accounting, Finance and Economics, University of Technology Mauritius, Pointe-Aux-Sables, Port Louis 11108, Mauritius
    These authors contributed equally to this work.)

Abstract

In real-life inter-related time series, the counting responses of different entities are commonly influenced by some time-dependent covariates, while the individual counting series may exhibit different levels of mutual over- or under-dispersion or mixed levels of over- and under-dispersion. In the current literature, there is still no flexible bivariate time series process that can model series of data of such types. This paper introduces a bivariate integer-valued autoregressive of order 1 (BINAR(1)) model with COM-Poisson innovations under time-dependent moments that can accommodate different levels of over- and under-dispersion. Another particularity of the proposed model is that the cross-correlation between the series is induced locally by relating the current observation of one series with the previous-lagged observation of the other series. The estimation of the model parameters is conducted via a Generalized Quasi-Likelihood (GQL) approach. The proposed model is applied to different real-life series problems in Mauritius, including transport, finance, and socio-economic sectors.

Suggested Citation

  • Naushad Mamode Khan & Yuvraj Sunecher, 2025. "A Flexible Bivariate Integer-Valued Autoregressive of Order (1) Model for Over- and Under-Dispersed Time Series Applications," Stats, MDPI, vol. 8(1), pages 1-25, March.
  • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:22-:d:1610824
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/8/1/22/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-905X/8/1/22/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:22-:d:1610824. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.