Bayesian Estimation of Unknown Regression Error Heteroscedasticity
AbstractWe 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 InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd08-051.
Date of creation: Mar 2009
Date of revision:
Eicker-White HCCM; orthogonal regressors; informative prior pdf's; MCMC; stock return variance;
Other versions of this item:
- Hiroaki Chigira & Tsunemasa Shiba, 2007. "Bayesian Estimation of Unknown Regression Error Heteroscedasticity," Hi-Stat Discussion Paper Series d07-221, Institute of Economic Research, Hitotsubashi University.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-04-18 (All new papers)
- NEP-ECM-2009-04-18 (Econometrics)
- NEP-ORE-2009-04-18 (Operations Research)
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- 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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