IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v113y2018i524p1722-1732.html
   My bibliography  Save this article

Over-Dispersed Age-Period-Cohort Models

Author

Listed:
  • Jonas Harnau
  • Bent Nielsen

Abstract

We consider inference and forecasting for aggregate data organized in a two-way table with age and cohort as indices, but without measures of exposure. This is modeled using a Poisson likelihood with an age-period-cohort structure for the mean while allowing for over-dispersion. We propose a repetitive structure that keeps the dimension of the table fixed while increasing the latent exposure. For this, we use a class of infinitely divisible distributions which include a variety of compound Poisson models and Poisson mixture models. This results in asymptotic F inference and t forecast distributions.

Suggested Citation

  • Jonas Harnau & Bent Nielsen, 2018. "Over-Dispersed Age-Period-Cohort Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1722-1732, October.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:524:p:1722-1732
    DOI: 10.1080/01621459.2017.1366908
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2017.1366908
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2017.1366908?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.
    2. Gao, Guangyuan & Meng, Shengwang & Shi, Yanlin, 2021. "Dispersion modelling of outstanding claims with double Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 572-586.
    3. Bischofberger, Stephan M. & Hiabu, Munir & Mammen, Enno & Nielsen, Jens Perch, 2019. "A comparison of in-sample forecasting methods," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 133-154.
    4. Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
    5. Steinmetz, Julia & Jentsch, Carsten, 2022. "Asymptotic theory for Mack's model," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 223-268.

    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:jnlasa:v:113:y:2018:i:524:p:1722-1732. 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/UASA20 .

    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.