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Bayesian Hypothesis Testing in Latent Variable Models

  • Yong Li

    ()

    (Business School, Sun Yat-Sen University)

  • Jun Yu

    ()

    (School of Economics, Singapore Management Unversity)

Hypothesis testing using Bayes factors (BFs) is known not to be well de ned 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.

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Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 11-2011.

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Length: 24 pages
Date of creation: Aug 2011
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:11-2011
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