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Bayesian Estimation of Unknown Regression Error Heteroscedasticity


  • Hiroaki Chigira
  • Tsunemasa Shiba


We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term, when the form of heteroscedasticity is unknown. We use prior information that is elicited from the well-known Eicker-White Heteroscedasticity Consistent Variance- CovarianceMatrix Estimator, and then useMarkov ChainMonte Carlo algorithm to simulate posterior pdf's of the unknown heteroscedastic variances. In addition to numerical examples, we present an empirical investigation of the stock prices of Japanese pharmaceutical and biomedical companies.

Suggested Citation

  • Hiroaki Chigira & Tsunemasa Shiba, 2007. "Bayesian Estimation of Unknown Regression Error Heteroscedasticity," Hi-Stat Discussion Paper Series d07-221, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hst:hstdps:d07-221

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    Cited by:

    1. Hiroaki Chigira & Tsunemasa Shiba, 2012. "Dirichlet Prior for Estimating Unknown Regression Error Heteroscedasticity," Global COE Hi-Stat Discussion Paper Series gd12-248, Institute of Economic Research, Hitotsubashi University.

    More about this item


    Eicker-White HCCM; orthogonal regressors; informative prior pdf's; MCMC; stock return variance;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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