IDEAS home Printed from https://ideas.repec.org/p/hst/ghsdps/gd12-248.html
   My bibliography  Save this paper

Dirichlet Prior for Estimating 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 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.

Suggested Citation

  • 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.
    • Handle: RePEc:hst:ghsdps:gd12-248
    as

    Download full text from publisher

    File URL: http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd12-248.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Greenberg,Edward, 2014. "Introduction to Bayesian Econometrics," Cambridge Books, Cambridge University Press, number 9781107436770, January.
    2. 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.
    3. Robinson, P M, 1987. "Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form," Econometrica, Econometric Society, vol. 55(4), pages 875-891, July.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    6. Godfrey, L.G., 2006. "Tests for regression models with heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2715-2733, June.
    7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    8. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    9. Chan, K C & Chen, Nai-Fu, 1991. "Structural and Return Characteristics of Small and Large Firms," Journal of Finance, American Finance Association, vol. 46(4), pages 1467-1484, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ruochen Wu & Melvyn Weeks, 2020. "A Semi-Parametric Bayesian Generalized Least Squares Estimator," Papers 2011.10252, arXiv.org, revised Jan 2023.
    2. Wu, R. & Weeks, M., 2020. "A Semi-Parametric Bayesian Generalized Least Square Estimator," Cambridge Working Papers in Economics 2011, Faculty of Economics, University of Cambridge.
    3. Doppelhofer, G. & Moe Hansen, O-P. & Weeks, M., 2017. "Determinants of long-term economic growth redux: A Measurement Error Model Averaging (MEMA) approach," Cambridge Working Papers in Economics 1702, Faculty of Economics, University of Cambridge.
    4. Doppelhofer, Gernot & Hansen, Ole-Petter Moe & Weeks, Melvyn, 2016. "Determinants of long-term economic Growth redux: A Measurement Error Model Averaging (MEMA) approach," Discussion Paper Series in Economics 19/2016, Norwegian School of Economics, Department of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amir Amel†Zadeh, 2011. "The Return of the Size Anomaly: Evidence from the German Stock Market," European Financial Management, European Financial Management Association, vol. 17(1), pages 145-182, January.
    2. Daphne Lui & Stanimir Markov & Ane Tamayo, 2007. "What Makes a Stock Risky? Evidence from Sell‐Side Analysts' Risk Ratings," Journal of Accounting Research, Wiley Blackwell, vol. 45(3), pages 629-665, June.
    3. Hrishikesh D. Vinod, 2008. "Heteroscedasticity and Autocorrelation Efficient (HAE) Estimation and Pivots for Jointly Evolving Series," Fordham Economics Discussion Paper Series dp2008-15, Fordham University, Department of Economics.
    4. Tuomo Vuolteenaho, 2002. "What Drives Firm‐Level Stock Returns?," Journal of Finance, American Finance Association, vol. 57(1), pages 233-264, February.
    5. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    6. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    7. Beladi, Hamid & Chao, Chi Chur & Hu, May, 2016. "Another January effect—Evidence from stock split announcements," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 123-138.
    8. Guo, Hui, 2006. "Time-varying risk premia and the cross section of stock returns," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2087-2107, July.
    9. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2011. "Multi-regime nonlinear capital asset pricing models," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1421-1438, April.
    10. Gorman, Larry, 2003. "Conditional performance, portfolio rebalancing, and momentum of small-cap mutual funds," Review of Financial Economics, Elsevier, vol. 12(3), pages 287-300.
    11. Keith Lam & Frank Li, 2008. "The risk premiums of the four-factor asset pricing model in the Hong Kong stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 18(20), pages 1667-1680.
    12. Bassetti, Federico & De Giuli, Maria Elena & Nicolino, Enrica & Tarantola, Claudia, 2018. "Multivariate dependence analysis via tree copula models: An application to one-year forward energy contracts," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1107-1121.
    13. Keith Vorkink & Douglas J. Hodgson & Oliver Linton, 2002. "Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 617-639.
    14. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Pricing default risk: The good, the bad, and the anomaly," Journal of Financial Stability, Elsevier, vol. 26(C), pages 190-213.
    15. Tienyu Hwang & Simon Gao & Heather Owen, 2014. "Markowitz efficiency and size effect: evidence from the UK stock market," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 721-750, November.
    16. Fernando Rubio, 2005. "Estrategias Cuantitativas De Valor Y Retornos Por Accion De Largo," Finance 0503029, University Library of Munich, Germany.
    17. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    18. Cem Ertur & Wilfried Koch, 2006. "The Role of Human Capital and Technological Interdependence in Growth and Convergence Processes: International Evidence," DEGIT Conference Papers c011_029, DEGIT, Dynamics, Economic Growth, and International Trade.
    19. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    20. Apergis, Nicholas & Payne, James E., 2014. "Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets," Review of Financial Economics, Elsevier, vol. 23(1), pages 46-53.

    More about this item

    Keywords

    Dirichlet prior; Eicker–White HCCM; informative prior pdf's; MCMC;
    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

    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:ghsdps:gd12-248. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.