IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v74y2000i2p282-295.html
   My bibliography  Save this article

Degeneracy in Heteroscedastic Regression Models

Author

Listed:
  • Li, Kim-Hung
  • Chan, Nai Ng

Abstract

The maximum likelihood estimation in a regression model with heteroscedastic errors is considered. When the design matrices in the model are inappropriately specified, the maximum likelihood estimates of the variances of certain observations are found to be zero irrespective of the observed values, resulting in degeneracy. Necessary and sufficient conditions for degeneracy are given and used for its avoidance.

Suggested Citation

  • Li, Kim-Hung & Chan, Nai Ng, 2000. "Degeneracy in Heteroscedastic Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 74(2), pages 282-295, August.
    • Handle: RePEc:eee:jmvana:v:74:y:2000:i:2:p:282-295
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(99)91887-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Murray Aitkin, 1987. "Modelling Variance Heterogeneity in Normal Regression Using GLIM," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 332-339, November.
    2. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    Full references (including those not matched with items on IDEAS)

    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. Cheng, Tsung-Chi, 2012. "On simultaneously identifying outliers and heteroscedasticity without specific form," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2258-2272.
    2. Tsung-Shan Tsou, 2005. "Inferences of variance function - a parametric robust way," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(8), pages 785-796.
    3. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
    4. Shelley A. Blozis & Ricardo Villarreal & Sweta Thota & Nicholas Imparato, 2019. "Using a two-part mixed-effects model for understanding daily, individual-level media behavior," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(4), pages 234-250, December.
    5. Liu-Cang Wu & Zhong-Zhan Zhang & Deng-Ke Xu, 2012. "Variable selection in joint mean and variance models of Box--Cox transformation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2543-2555, August.
    6. Cysneiros, Francisco José A. & Paula, Gilberto A. & Galea, Manuel, 2007. "Heteroscedastic symmetrical linear models," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1084-1090, June.
    7. Xu, Dengke & Zhang, Zhongzhan, 2013. "A semiparametric Bayesian approach to joint mean and variance models," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1624-1631.
    8. Yeşim Güney & Yetkin Tuaç & Şenay Özdemir & Olcay Arslan, 2021. "Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution," Computational Statistics, Springer, vol. 36(2), pages 805-827, June.
    9. Liucang Wu & Huiqiong Li, 2012. "Variable selection for joint mean and dispersion models of the inverse Gaussian distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(6), pages 795-808, August.
    10. Donald Hedeker & Robin J. Mermelstein & Hakan Demirtas, 2008. "An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data," Biometrics, The International Biometric Society, vol. 64(2), pages 627-634, June.
    11. Afrânio M.C. Vieira & Roseli A. Leandro & Clarice G.B. Dem�trio & Geert Molenberghs, 2011. "Double generalized linear model for tissue culture proportion data: a Bayesian perspective," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1717-1731, September.
    12. George Leckie & Robert French & Chris Charlton & William Browne, 2014. "Modeling Heterogeneous Variance–Covariance Components in Two-Level Models," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 307-332, October.
    13. Brown, Sarah & Greene, William H. & Harris, Mark N. & Taylor, Karl, 2015. "An inverse hyperbolic sine heteroskedastic latent class panel tobit model: An application to modelling charitable donations," Economic Modelling, Elsevier, vol. 50(C), pages 228-236.
    14. Jenkins, Robin R. & Martinez, Salvador A. & Palmer, Karen & Podolsky, Michael J., 2003. "The determinants of household recycling: a material-specific analysis of recycling program features and unit pricing," Journal of Environmental Economics and Management, Elsevier, vol. 45(2), pages 294-318, March.
    15. Panayi, Efstathios & Peters, Gareth W. & Danielsson, Jon & Zigrand, Jean-Pierre, 2018. "Designating market maker behaviour in limit order book markets," Econometrics and Statistics, Elsevier, vol. 5(C), pages 20-44.
    16. Anjum, Zeba & Burke, Paul J. & Gerlagh, Reyer & Stern, David I., "undated". "Modeling the Emissions-Income Relationship Using Long-Run Growth Rates," Working Papers 249422, Australian National University, Centre for Climate Economics & Policy.
    17. Ryan A. Decker & Pablo N. D'Erasmo & Hernan Moscoso Boedo, 2016. "Market Exposure and Endogenous Firm Volatility over the Business Cycle," American Economic Journal: Macroeconomics, American Economic Association, vol. 8(1), pages 148-198, January.
    18. Ansgar Belke & Robert Czudaj, 2010. "Is Euro Area Money Demand (Still) Stable? Cointegrated VAR Versus Single Equation Techniques," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 56(4), pages 285-315.
    19. repec:zbw:bofrdp:2018_017 is not listed on IDEAS
    20. Stacy, Brian, 2014. "Ranking Teachers when Teacher Value-Added is Heterogeneous Across Students," EconStor Preprints 104743, ZBW - Leibniz Information Centre for Economics.
    21. Xiaolu Tang & César Pérez-Cruzado & Lutz Fehrmann & Juan Gabriel Álvarez-González & Yuanchang Lu & Christoph Kleinn, 2016. "Development of a Compatible Taper Function and Stand-Level Merchantable Volume Model for Chinese Fir Plantations," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-15, January.

    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:eee:jmvana:v:74:y:2000:i:2:p:282-295. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.