IDEAS home Printed from
   My bibliography  Save this paper

Heteroskedasticity Testing Through Comparison of Wald-Type Statistics


  • José Murteira

    () (Faculdade de Economia Universidade de Coimbra / CEMAPRE)

  • Esmeralda Ramalho

    (Departamento de Economia and CEFAGE-UE, Universidade de Évora)

  • Joaquim Ramalho

    (Departamento de Economia and CEFAGE-UE, Universidade de Évora)


A test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The resulting statistic is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom. The power of this test is sensitive to the choice of parametric restriction on which the Wald statistics are based, so the supremum of a range of individual test statistics is proposed. Two versions of a supremum-based test are considered: the first version, easier to implement, does not have a known asymptotic null distribution, so the bootstrap is employed in order to assess its behaviour and enable meaningful conclusions from its use in applied work. The second version has a known asymptotic distribution and, in some cases, is asymptotically pivotal under the null. A small simulation study illustrates the implementation and finite-sample performance of both versions of the test.

Suggested Citation

  • José Murteira & Esmeralda Ramalho & Joaquim Ramalho, 2011. "Heteroskedasticity Testing Through Comparison of Wald-Type Statistics," GEMF Working Papers 2011-05, GEMF, Faculty of Economics, University of Coimbra.
  • Handle: RePEc:gmf:wpaper:2011-05

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    3. Godfrey, Leslie G., 1996. "Some results on the Glejser and Koenker tests for heteroskedasticity," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 275-299.
    4. L. G. Godfrey & C. D. Orme & J. M. C. Santos Silva, 2006. "Simulation-based tests for heteroskedasticity in linear regression models: Some further results," Econometrics Journal, Royal Economic Society, vol. 9(1), pages 76-97, March.
    5. Machado, Jose A. F. & Silva, J. M. C. Santos, 2000. "Glejser's test revisited," Journal of Econometrics, Elsevier, vol. 97(1), pages 189-202, July.
    6. 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.
    7. Im, Kyung So, 2000. "Robustifying Glejser test of heteroskedasticity," Journal of Econometrics, Elsevier, vol. 97(1), pages 179-188, July.
    8. Godfrey, Leslie G., 1978. "Testing for multiplicative heteroskedasticity," Journal of Econometrics, Elsevier, vol. 8(2), pages 227-236, October.
    9. L. G. Godfrey & C. D. Orme, 1999. "The robustness, reliabiligy and power of heteroskedasticity tests," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 169-194.
    10. Koenker, Roger, 1981. "A note on studentizing a test for heteroscedasticity," Journal of Econometrics, Elsevier, vol. 17(1), pages 107-112, September.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Heteroskedasticity testing; White test; Wald test; Supremum;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:gmf:wpaper:2011-05. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ana Seiça). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.