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Bayesian hierarchical models in Stata


  • Nikolay Balov

    (StataCorp LP)


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

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    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.

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