IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v47y2018i5p1133-1154.html
   My bibliography  Save this article

A BINAR(1) time-series model with cross-correlated COM–Poisson innovations

Author

Listed:
  • V. Jowaheer
  • N. Mamode Khan
  • Y. Sunecher

Abstract

This article proposes a bivariate integer-valued autoregressive time-series model of order 1 (BINAR(1) with COM–Poisson marginals to analyze a pair of non stationary time series of counts. The interrelation between the series is induced by the correlated innovations, while the non stationarity is captured through a common set of time-dependent covariates that influence the count responses. The regression and dependence effects are estimated using generalized quasi-likelihood (GQL) approach. Simulation experiments are performed to assess the performance of the estimation algorithms. The proposed BINAR(1) process is applied to analyze a real-life series of day and night accidents in Mauritius.

Suggested Citation

  • V. Jowaheer & N. Mamode Khan & Y. Sunecher, 2018. "A BINAR(1) time-series model with cross-correlated COM–Poisson innovations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(5), pages 1133-1154, March.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:5:p:1133-1154
    DOI: 10.1080/03610926.2017.1316400
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2017.1316400
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2017.1316400?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    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:taf:lstaxx:v:47:y:2018:i:5:p:1133-1154. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.