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Bayesian Hypothesis Testing in Latent Variable Models

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Author Info

  • Yong Li

    ()
    (Business School, Sun Yat-Sen University)

  • Jun Yu

    ()
    (School of Economics, Singapore Management Unversity)

Abstract

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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Bibliographic Info

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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Related research

Keywords: Bayes factors; Kullback-Leibler divergence; Decision theory; EM Algorithm; Markov Chain Monte Carlo;

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References

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Citations

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Cited by:
  1. Pan, Qi & Li, Yong, 2013. "Testing volatility persistence on Markov switching stochastic volatility models," Economic Modelling, Elsevier, vol. 35(C), pages 45-50.
  2. Yong Li & Tao Zeng & Jun Yu, 2012. "Robust Deviance Information Criterion for Latent Variable Models," Working Papers 30-2012, Singapore Management University, School of Economics.
  3. Jin-Yu Zhang & Yong Li & Zhu-Ming Chen, 2013. "Unit Root Hypothesis in the Presence of Stochastic Volatility, a Bayesian Analysis," Computational Economics, Society for Computational Economics, vol. 41(1), pages 89-100, January.
  4. Cathy Chen & Shu-Yu Chen & Sangyeol Lee, 2013. "Bayesian Unit Root Test in Double Threshold Heteroskedastic Models," Computational Economics, Society for Computational Economics, vol. 42(4), pages 471-490, December.
  5. Li, Yong & Zeng, Tao & Yu, Jun, 2014. "A new approach to Bayesian hypothesis testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 602-612.
  6. Li, Yong & Chong, Terence Tai-Leung & Zhang, Jie, 2012. "Testing for a unit root in the presence of stochastic volatility and leverage effect," Economic Modelling, Elsevier, vol. 29(5), pages 2035-2038.

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