Publication Type

Journal Article

Publication Date

2-2012

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 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.

Keywords

Bayes factors, Kullback-Leibler divergence, Decision theory, EM algorithm, Markov chain Monte Carlo

Discipline

Econometrics | Finance

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

166

Issue

2

First Page

237

Last Page

246

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2011.09.040

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.jeconom.2011.09.040

Share

COinS