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Testing for heteroscedasticity in regression models

Author

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  • Maria Carapeto
  • William Holt

Abstract

A new test for heteroscedasticity in regression models is presented based on the Goldfeld-Quandt methodology. Its appeal derives from the fact that no further regressions are required, enabling widespread use across all types of regression models. The distribution of the test is computed using the Imhof method and its power is assessed by performing a Monte Carlo simulation. We compare our results with those of Griffiths & Surekha (1986) and show that our test is more powerful than the wide range of tests they examined. We introduce an estimation procedure using a neural network to correct the heteroscedastic disturbances.

Suggested Citation

  • Maria Carapeto & William Holt, 2003. "Testing for heteroscedasticity in regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(1), pages 13-20.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:13-20
    DOI: 10.1080/0266476022000018475
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    Cited by:

    1. Reşit Çelik, 2017. "A new test to detect monotonic and non-monotonic types of heteroscedasticity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 342-361, January.
    2. Resit Çelik, 2015. "Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 705-721, April.
    3. James Cameron & Pramita Bagchi, 2022. "A test for heteroscedasticity in functional linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 519-542, June.

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