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.
Bayes factors, Kullback-Leibler divergence, Decision theory, EM Algorithm, Markov Chain Monte Carlo.
Econometrics | Finance
LI, Yong and YU, Jun.
Bayesian Hypothesis Testing in Latent Variable Models. (2010). 14-2010, 1-28. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1233
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