Dirichlet Prior for Estimating
Unknown Regression Error Heteroscedasticity

Hiroaki Chigira
Tsunemasa Shiba

October 2012


We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term ω, when the form of heteroscedasticity is unknown. The prior information on ω is based on a Dirichlet distribution, and in the Markov Chain Monte Carlo sampling, its proposal density parameters' information is elicited from the well-known Eicker-White Heteroscedasticity Consistent Variance-Covariance Matrix Estimator. We present a numerical example to show that our scheme works.

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