Publication Type
Working Paper
Version
publishedVersion
Publication Date
9-2013
Abstract
This paper extends the robust Bayesian inference in misspecified models of Müller (2013, Econometrica) to Bayesian model selection of a set of misspecified models. It is shown that when a model is misspecified, under the Kullback-Leibler loss function, the risk associated with Müller's posterior is less (weakly) than that with the original posterior distribution asymptotically. Based on this new result, two new information criteria are proposed for model selection under model misspecification. Sufficient conditions are provided for the risk associated with Müller's posterior to be strictly smaller.
Keywords
Model selection, Model misspecification, Artificial posterior distribution, Sandwich-covariance matrix; Markov chain Monte Carlo
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
27
Citation
LI, Yong and YU, Jun.
Robust Bayesian model selection. (2013). 1-27.
Available at: https://ink.library.smu.edu.sg/soe_research/2112
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.