Dirichlet Prior for Estimating Unknown Regression Error Heteroskedasticity

Hiroaki Chigira and Tsunemasa Shiba

No 341, TERG Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: We propose a Bayesian procedure to estimate heteroskedastic variances of the regression error term ?O, when the form of heteroskedasticity is unknown. The prior information on ?O 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 Heteroskedasticity Consistent Variance-Covariance Matrix Estimator. We present an emprical example to show that our scheme works.

Pages: 17 pages
Date: 2015-12-22
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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http://hdl.handle.net/10097/62609

Related works:
Working Paper: DIRICHLET PRIOR FOR ESTIMATING UNKNOWN REGRESSION ERROR HETEROSKEDASTICITY (2015)
Working Paper: Dirichlet Prior for Estimating Unknown Regression Error Heteroscedasticity (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:toh:tergaa:341

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