The Large Sample Correspondence Between Classical Hypothesis Tests and Bayesian Posterior Odds Tests
Donald Andrews ()
No 1035, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
Abstract:
This paper establishes a correspondence in large samples between classical hypothesis tests and Bayesian posterior odds tests for models without trends. More specifically, tests of point null hypotheses and one- or two-sided alternatives are considered (where nuisance parameters may be present under both hypotheses). It is shown that for certain priors the Bayesian posterior odds test is equivalent in large samples to classical Wald, Lagrange multiplier, and likelihood ratio tests for some significance level and vice versa.
Keywords: Asymptotics; Bayesian; classical; hypothesis test; likelihood ratio; posterior odds; prior (search for similar items in EconPapers)
Pages: 38 pages
Date: 1992-11
Note: CFP 874.
References: Add references at CitEc
Citations:
Published in Econometrica (September 1994), 62(5): 1207-1232
Downloads: (external link)
https://cowles.yale.edu/sites/default/files/files/pub/d10/d1035.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found
Related works:
Journal Article: The Large Sample Correspondence between Classical Hypothesis Tests and Bayesian Posterior Odds Tests (1994) 
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:cwl:cwldpp:1035
Ordering information: This working paper can be ordered from
Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
The price is None.
Access Statistics for this paper
More papers in Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University Yale University, Box 208281, New Haven, CT 06520-8281 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Brittany Ladd ().