Publication Type
Working Paper
Version
publishedVersion
Publication Date
3-2023
Abstract
Hypothesis testing via p-value has been criticized in recent years. Bayes factors (BFs) have been tipped as a possible replacement of p-value for hypothesis testing. However, the standard BFs suffer from some theoretical and practical difficulties. For example, they are not well defined under improper priors and are subject to Jeffreys-Lindley-Bartlett’s paradox under vague priors. Moreover, they are difficult to compute for many models. In this paper, we propose to compare sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based BFs are constructed from the posterior version of the likelihood ratio test and the Wald test, respectively. Under regularity conditions, we show that the new methods can avoid the p-hacking problem and the problems in the standard BFs. The advantages of the proposed methods are investigated using several simulation and empirical studies.
Keywords
Bayes factor, Consistency, p-value, p-hacking, Posterior likelihood ratio test, Posterior Wald test
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
48
Publisher
Paper No. 06-2023
Citation
LI, Yong; WANG, Nianling; Jun YU; and ZHANG, Yonghui.
Hypothesis testing via posterior-test-based Bayes factors. (2023). 1-48.
Available at: https://ink.library.smu.edu.sg/soe_research/2671
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.