Publication Type
Book Chapter
Version
acceptedVersion
Publication Date
8-2019
Abstract
This chapter overviews several MCMC-based test statistics for hypothesis testing andspecification testing and MCMC-based model selection criteria developed in recentyears. The statistics for hypothesis testing can be viewed as the MCMC version ofthe “trinity” of test statistics based in maximum likelihood (ML), namely, the likelihoodratio (LR) test, the Lagrange multiplier (LM) test, and the Wald test. The model selection criteria correspond to two predictive distributions. One of them can be viewed asthe MCMC version of widely used information criterion, AIC. The asymptotic distributions of the test statistics and model selection criteria are discussed. The test statisticsand model selection criteria are applied to several popular models using real data,one of which involves latent variables. The implementation is illustrated in R withthe MCMC output obtained by R2WinBUGS.
Keywords
AIC, DIC, Information matrix, LR test, LM test, Markov chain Monte Carlo, Latent variable, Wald test
Discipline
Econometrics
Research Areas
Econometrics
Publication
Handbook of Statistics Vol 41
Volume
41
Editor
Hrishikesh D. Vinod; C.R. Rao
First Page
81
Last Page
115
ISBN
9780444643117
Identifier
10.1016/bs.host.2018.12.003
Publisher
Elsevier
Citation
LI, Yong; YU, Jun; and ZENG, Tao.
Hypothesis testing, specification testing and model selection based on the MCMC output using R. (2019). Handbook of Statistics Vol 41. 41, 81-115.
Available at: https://ink.library.smu.edu.sg/soe_research/2321
Copyright Owner and License
2019 Elsevier B.V.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/bs.host.2018.12.003