Publication Type
Working Paper
Version
publishedVersion
Publication Date
8-2011
Abstract
Hypothesis testing using Bayes factors (BFs) is known not to be well defined under the improper prior. In the context of latent variable models, an additional problem with BFs is that they are difficult to compute. In this paper, a new Bayesian method, based on decision theory and the EM algorithm, is introduced to test a point hypothesis in latent variable models. The new statistic is a by-product of the Bayesian MCMC output and, hence, easy to compute. It is shown that the new statistic is easy to interpret and appropriately defined under improper priors because the method employs a continuous loss function. The method is illustrated using a one-factor asset pricing model and a stochastic volatility model with jumps.
Keywords
Bayes factors, Kullback-Leibler divergence, Decision theory, EM Algorithm, Markov Chain Monte Carlo.
Discipline
Econometrics
Research Areas
Econometrics
Volume
11-2011
First Page
1
Last Page
23
Publisher
SMU Economics and Statistics Working Paper Series, No. 11-2011
City or Country
Singapore
Citation
LI, Yong and YU, Jun.
Bayesian Hypothesis Testing in Latent Variable Models. (2011). 11-2011, 1-23.
Available at: https://ink.library.smu.edu.sg/soe_research/1303
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.