IDEAS home Printed from https://ideas.repec.org/p/boc/scon16/30.html
   My bibliography  Save this paper

Bayesian hierarchical models in Stata

Author

Listed:
  • Nikolay Balov

    (StataCorp LP)

Abstract

Bayesian analysis is a flexible statistical methodology for inferring properties of unknown parameters by combining observational evidence with prior knowledge. Research questions are answered using explicit probability statements. The Bayesian approach is especially well suited for analyzing data models in which the data structure imposes a model parameter hierarchy. Stata 14 introduces a suite of commands for specification and simulation of Bayesian models, computing various posterior summaries, testing hypotheses, and comparing models. I will describe the main features of these commands and present examples illustrating various models, from a simple logistic regression to hierarchical Rasch models.

Suggested Citation

  • Nikolay Balov, 2016. "Bayesian hierarchical models in Stata," 2016 Stata Conference 30, Stata Users Group.
    • Handle: RePEc:boc:scon16:30
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/chic2016/chicago16_balov.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Gebremeskel Berhane Tesfay & Babatunde Abidoye, 2019. "Shocks in food availability and intra-household resources allocation: evidence on children nutrition outcomes in Ethiopia," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-21, December.

    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:boc:scon16:30. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    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.