Publication Type

Working Paper

Publication Date

10-2010

Abstract

Hypothesis testing using Bayes factors (BFs) is known to suffer from several problems in the context of latent variable models. The first problem is computational. Another problem is that BFs are not well defined under the improper prior. 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 appropriately defined under improper priors because the method employs a continuous loss function. The finite sample properties are examined using simulated data. The method is also illustrated in the context of a one-factor asset pricing model and a stochastic volatility model with jumps using real data.

Keywords

Bayes factors, Kullback-Leibler divergence, Decision theory, EM Algorithm, Markov Chain Monte Carlo.

Discipline

Econometrics | Finance

Research Areas

Econometrics

Volume

14-2010

First Page

1

Last Page

28

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.

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