TESTING SIGNIFICANCE OF VARIABLES IN REGRESSION ANALYSIS WHEN THERE IS NON-NORMALITY OR HETEROSKEDASTICITY.: THE WILD BOOTSTRAP AND THE GENERALISED LAMBDA DISTRIBUTION

Efthymios Pavlidis, Ivan Paya () and David Peel

Chapter 8 in Advances in Doctoral Research in Management, 2008, pp 151-174 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractStatistical 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.

Keywords: Doctoral; Research; Management Methodology; Data; Analysis; Paradigm; Modeling; International; Management Theory; Statistics; Market Survey (search for similar items in EconPapers)
JEL-codes: F1 (search for similar items in EconPapers)
Date: 2008
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