IDEAS home Printed from https://ideas.repec.org/p/hst/hstdps/d07-221.html
   My bibliography  Save this paper

Bayesian Estimation of Unknown Regression Error Heteroscedasticity

Author

Listed:
  • Hiroaki Chigira
  • Tsunemasa Shiba

Abstract

We propose a Bayesian procedure to estimate heteroscedastic variances of the regression error term, when the form of heteroscedasticity is unknown. We use prior information that is elicited from the well-known Eicker-White Heteroscedasticity Consistent Variance- CovarianceMatrix Estimator, and then useMarkov ChainMonte Carlo algorithm to simulate posterior pdf's of the unknown heteroscedastic variances. In addition to numerical examples, we present an empirical investigation of the stock prices of Japanese pharmaceutical and biomedical companies.

Suggested Citation

  • 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.
    • Handle: RePEc:hst:hstdps:d07-221
    as

    Download full text from publisher

    File URL: http://hi-stat.ier.hit-u.ac.jp/research/discussion/2007/pdf/D07-221.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    More about this item

    Keywords

    Eicker-White HCCM; orthogonal regressors; informative prior pdf's; MCMC; stock return variance;
    All these keywords.

    JEL classification:

    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hst:hstdps:d07-221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tatsuji Makino (email available below). General contact details of provider: https://edirc.repec.org/data/iehitjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.