Dirichlet Prior for Estimating 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 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.

Keywords: Dirichlet prior; Eicker–White HCCM; informative prior pdf's; MCMC (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
Date: 2012-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd12-248.pdf (application/pdf)

Related works:
Working Paper: DIRICHLET PRIOR FOR ESTIMATING UNKNOWN REGRESSION ERROR HETEROSKEDASTICITY (2015)
Working Paper: Dirichlet Prior for Estimating Unknown Regression Error Heteroskedasticity (2015)
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