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Simulation-based tests for heteroskedasticity in linear regression models: Some further results

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  • L. G. Godfrey
  • C. D. Orme
  • J. M. C. Santos Silva

Abstract

As shown by the results of Dufour, Khalaf, Bernard and Genest (2004, Journal of Econometrics 122, 317--347), exact tests for heteroskedasticity in linear regression models can be obtained, by using Monte Carlo (MC) techniques, if either (i) it is assumed that the true form of the error distribution under homoskedasticity is known, or (ii) the null hypothesis specifies both homoskedasticity and the form of the error distribution. Non-parametric bootstrap tests of homoskedasticity alone are only asymptotically valid, but do not require specification of the error law. Since information about the precise form of the error distribution is not often available to applied workers, two questions merit attention. First, if the primary purpose is to check for heteroskedasticity, how sensitive are MC tests to incorrect assumptions/claims about the error distribution? Second, what can be said about the relative merits of MC tests and non-parametric bootstrap tests? Theoretical results relevant to these two questions are derived using asymptotic analysis and evidence is provided from simulation experiments. Copyright 2006 Royal Economic Society

Suggested Citation

  • 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.
    • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:1:p:76-97
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    Cited by:

    1. José Murteira & Esmeralda Ramalho & Joaquim Ramalho, 2013. "Heteroskedasticity testing through a comparison of Wald statistics," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 12(2), pages 131-160, August.
    2. 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.
    3. 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).
    4. Chris D. Orme & Takashi Yamagata, 2014. "A Heteroskedasticity-Robust F -Test Statistic for Individual Effects," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 431-471, August.
    5. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Paper 1127, Economics Department, Queen's University.
    6. E. Fe-Rodríguez & C. Orme, 2006. "On the sensitivity of Kernel-based Conditional Moment Tests to Unconsidered Local Alternatives," Economics Discussion Paper Series 0606, Economics, The University of Manchester.

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