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Bayesian Estimation of Unknown Regression Error Heteroscedasticity

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  • Hiroaki Chigira
  • Tsunemasa Shiba

Abstract

We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term ƒÖ, when the form of heteroscedasticity is unknown. The prior information on ƒÖ is elicited from the wellknown Eicker-White Heteroscedasticity Consistent Variance-Covariance Matrix Estimator. Markov Chain Monte Carlo algorithm is used to simulate posterior pdffs of the unknown elements of ƒÖ. In addition to the numerical examples, we present an empirical investigation of the stock prices of Japanese pharmaceutical and biomedical companies to demonstrate usefulness of the proposed method.

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Bibliographic Info

Paper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd08-051.

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Date of creation: Mar 2009
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Handle: RePEc:hst:ghsdps:gd08-051

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

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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.

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