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Heteroskedasticity Testing Through Comparison of Wald-Type Statistics

Author

Listed:
  • 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)

Abstract

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
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Heteroskedasticity testing; White test; Wald test; Supremum;
    All these keywords.

    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

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