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Testing Significance Of Variables In Regression Analysis When There Is Non-Normality Or Heteroskedasticity.: The Wild Bootstrap And The Generalised Lambda Distribution

In: Advances In Doctoral Research In Management

Author

Listed:
  • E. Pavlidis

    (University of Lancaster Management School, UK)

  • I. Paya

    (University of Lancaster Management School, UK)

  • D. A. Peel

    (University of Lancaster Management School, UK)

Abstract

Statistical inference on the parameters of regression models requires special precautions when the error term is heteroskedastic and/or non-normal. In this case, although conventional test statistics do not follow t and F distributions, simulation methods can be used to draw inferences. We discuss two methods: the wild bootstrap and the generalised lambda distribution. By employing both artificial and real-world data from the National Footbal League, we show that these methods may prove particularly useful in hypothesis testing.

Suggested Citation

  • E. Pavlidis & I. Paya & D. A. Peel, 2008. "Testing Significance Of Variables In Regression Analysis When There Is Non-Normality Or Heteroskedasticity.: The Wild Bootstrap And The Generalised Lambda Distribution," World Scientific Book Chapters, in: Luiz Moutinho & Kun-Huang Huarng (ed.), Advances In Doctoral Research In Management, chapter 8, pages 151-174, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812778666_0008
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    More about this item

    Keywords

    Doctoral; Research; Management Methodology; Data; Analysis; Paradigm; Modeling; International; Management Theory; Statistics; Market Survey;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

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