A review and comparison of methods of testing for heteroskedasticity in the linear regression model

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dc.contributor.authorBlignaut, Renette
dc.contributor.authorLuus, Retha
dc.contributor.authorSteel, Sarel
dc.date.accessioned2025-12-18T07:48:22Z
dc.date.available2025-12-18T07:48:22Z
dc.date.issued2025
dc.description.abstractThis study reviews inferential methods for diagnosing heteroskedasticity in the linear regression model, classifying the methods into four types: deflator tests, auxiliary design tests, omnibus tests, and portmanteau tests. A Monte Carlo simulation experiment is used to compare the performance of deflator tests and the performance of auxiliary design and omnibus tests, using the metric of average excess power over size. Certain lesser-known tests (that are not included with some standard statistical software) are found to outperform better-known tests. For instance, the best-performing deflator test was the Evans-King test, and the best-performing auxiliary design and omnibus tests were Verbyla's test and the Cook-Weisberg test, and not standard methods such as White's test and the Breusch-Pagan-Koenker test.
dc.identifier.citationFarrar, T., Blignaut, R., Luus, R. and Steel, S., 2025. A review and comparison of methods of testing for heteroskedasticity in the linear regression model. Journal of Applied Statistics, 52(16), pp.3121-3150.
dc.identifier.urihttps://doi.org/10.1080/02664763.2025.2575038
dc.identifier.urihttps://hdl.handle.net/10566/21594
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.subjectDiagnostics
dc.subjectHeteroskedasticity
dc.subjectLinear
dc.subjectRegression
dc.subjectVariance
dc.titleA review and comparison of methods of testing for heteroskedasticity in the linear regression model
dc.typeArticle

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