IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v31y2016i6p1140-1158.html
   My bibliography  Save this article

Estimating Health Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model

Author

Listed:
  • Arnab Mukherji
  • Satrajit Roychoudhury
  • Pulak Ghosh
  • Sarah Brown

Abstract

No abstract is available for this item.

Suggested Citation

  • Arnab Mukherji & Satrajit Roychoudhury & Pulak Ghosh & Sarah Brown, 2016. "Estimating Health Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1140-1158, September.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:6:p:1140-1158
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2022. "A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study," Computational Statistics, Springer, vol. 37(2), pages 837-863, April.
    2. Jayabrata Biswas & Pulak Ghosh & Kiranmoy Das, 2020. "A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 261-283, June.
    3. Priya Kedia & Damitri Kundu & Kiranmoy Das, 2023. "A Bayesian variable selection approach to longitudinal quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 149-168, March.
    4. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    5. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.
    7. Anne Mason & Idaira Rodriguez Santana & María José Aragón & Nigel Rice & Martin Chalkley & Raphael Wittenberg & Jose-Luis Fernandez, 2019. "Drivers of health care expenditure: Final report," Working Papers 169cherp, Centre for Health Economics, University of York.

    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:wly:japmet:v:31:y:2016:i:6:p:1140-1158. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.