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Robust methods for heteroskedastic regression

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  • Atkinson, Anthony C.
  • Riani, Marco
  • Torti, Francesca

Abstract

Heteroskedastic regression data are modelled using a parameterized variance function. This procedure is robustified using a method with high breakdown point and high efficiency, which provides a direct link between observations and the weights used in model fitting. This feature is vital for the application, the analysis of international trade data from the European Union. Heteroskedasticity is strongly present in such data, as are outliers. A further example shows that the new method outperforms ordinary least squares with heteroskedasticity robust standard errors, even when the form of heteroskedasticity is mis-specified. A discussion of computational matters concludes the paper. An appendix presents the new scoring algorithm for estimation of the parameters of heteroskedasticity.

Suggested Citation

  • Atkinson, Anthony C. & Riani, Marco & Torti, Francesca, 2016. "Robust methods for heteroskedastic regression," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 209-222.
    • Handle: RePEc:eee:csdana:v:104:y:2016:i:c:p:209-222
    DOI: 10.1016/j.csda.2016.07.002
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    References listed on IDEAS

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    Cited by:

    1. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.

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