Bayesian Estimation of Unknown Regression Error Heteroscedasticity

Hiroaki Chigira and Tsunemasa Shiba

Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

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 pdf fs 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.

Keywords: Eicker-White HCCM; orthogonal regressors; informative prior pdf's; MCMC; stock return variance (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
Date: 2009-03
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Bayesian Estimation of Unknown Regression Error Heteroscedasticity (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:hst:ghsdps:gd08-051

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