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The robustness, reliabiligy and power of heteroskedasticity tests

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  • L. G. Godfrey
  • C. D. Orme

Abstract

Several tests for heteroskedasticity in linear regression models are examined. Asymptoticrobustness to heterokurticity, nonnormality and skewness is discussed. The finite sample eliability of asymptotically valid tests is investigated using Monte Carlo experiments. It is found that asymptotic critical values cannot, in general. be relied upon to give good agreement between nominal and actual finite sample significance levels. The use of the bootstrap overcomes this problem for general approaches that lead to asymptotically pivotal test statistics. Power comparisons are made for bootstrap tests and modified Glejser and Koenker tests are recommended.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:169-194
    DOI: 10.1080/07474939908800438
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    References listed on IDEAS

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    1. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    2. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(3), pages 361-376, June.
    3. Davidson, Russell & MacKinnon, James G., 1996. "The Power of Bootstrap Tests," Queen's Institute for Economic Research Discussion Papers 273372, Queen's University - Department of Economics.
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    Citations

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    Cited by:

    1. 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.
    2. Leslie G. Godfrey, 2005. "Controlling the Overall Significance Level of a Battery of Least Squares Diagnostic Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(2), pages 263-279, April.
    3. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
    4. 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.
    5. Godfrey, Leslie G. & Orme, Chris D., 2004. "Controlling the finite sample significance levels of heteroskedasticity-robust tests of several linear restrictions on regression coefficients," Economics Letters, Elsevier, vol. 82(2), pages 281-287, February.
    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. Gignac, Gilles E. & Zajenkowski, Marcin, 2020. "The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data," Intelligence, Elsevier, vol. 80(C).

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    More about this item

    Keywords

    heteroskedasticity; robustness; nonnormality; bootstrap; JEL Classification:C12; C52;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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