Bayesian hierarchical models in Stata

Nikolay Balov ()
Additional contact information
Nikolay Balov: StataCorp LP

2016 Stata Conference from Stata Users Group

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.

Date: 2016-08-10
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://fmwww.bc.edu/repec/chic2016/chicago16_balov.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:scon16:30

Access Statistics for this paper

More papers in 2016 Stata Conference from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().