IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v14y2026i13p2289-d1977331.html

Empirical Likelihood Statistical Inference for the BINAR(1) Process

Author

Listed:
  • Yan Liu

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China)

  • Yifei Wang

    (School of Mathematics and Statistics, Changchun University, Changchun 130022, China)

  • Bo Shao

    (Department of Sports Science and Physical Education, Changchun University of Science and Technology, Changchun 130600, China)

Abstract

In this paper, we apply the empirical likelihood (EL) approach to the bivariate first-order integer-valued autoregressive (BINAR(1)) model. The maximum empirical likelihood (MEL) estimator for this process is constructed by the empirical likelihood ratio (ELR) statistic derived from the EL method. We then discuss the asymptotic properties of this estimator. Subsequently, the model parameters are estimated using the MEL method, the conditional least squares method, and the conditional maximum likelihood method. The finite-sample performance of the proposed estimation procedures is examined through Monte Carlo simulation experiments. A real data example is further presented.

Suggested Citation

  • Yan Liu & Yifei Wang & Bo Shao, 2026. "Empirical Likelihood Statistical Inference for the BINAR(1) Process," Mathematics, MDPI, vol. 14(13), pages 1-26, June.
    • Handle: RePEc:gam:jmathe:v:14:y:2026:i:13:p:2289-:d:1977331
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/14/13/2289/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/14/13/2289/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jmathe:v:14:y:2026:i:13:p:2289-:d:1977331. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.