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 the 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 appropriately defined under improper priors because the method employs a continuous loss function. In addition, it is easy to interpret. The method is illustrated using a one-factor asset pricing model and a stochastic volatility model with jumps.
Bayes factors, Kullback-Leibler divergence, Decision theory, EM algorithm, Markov chain Monte Carlo
Econometrics | Finance
Journal of Econometrics
LI, Yong and YU, Jun.
Bayesian Hypothesis Testing in Latent Variable Models. (2012). Journal of Econometrics. 166, (2), 237-246. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1316
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.