Publication Type
Working Paper
Version
publishedVersion
Publication Date
6-2014
Abstract
A new Bayesian test statistic is proposed to test a point null hypothesis based on of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appeal in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it is relatively easy to compute, even for models with latent variables. Finally, it is pivotal and its threshold value can be easily obtained from the asymptotic chi-squared distribution. The method is illustrated using some real examples in economics and finance.
Keywords
Bayes factor; Decision theory; EM algorithm; Lagrange multiplier; Markov chain Monte Carlo; Latent variable models.
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
28
Publisher
SMU Economics and Statistics Working Paper Series, No. 03-2014
City or Country
Singapore
Citation
LI, Yong; LIU, Xiao-Bin; and YU, Jun.
A Bayesian Chi-Squared Test for Hypothesis Testing. (2014). 1-28.
Available at: https://ink.library.smu.edu.sg/soe_research/1588
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics https://doi.org/10.1016/j.jeconom.2015.06.021