Using OLS to test for normality
The OLS estimator is a weighted average of the slopes delineated by adjacent observations. These weights depend only on the independent variable. Equal weights are obtained if and only if the independent variable is normally distributed. This feature is used to develop a new test for normality which is compared to standard tests and provides better power for testing normality.
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Volume (Year): 82 (2012)
Issue (Month): 11 ()
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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"Testing Normality : A GMM Approach,"
Cahiers de recherche
2002-14, Universite de Montreal, Departement de sciences economiques.
- Heckman, James J. & Urzua, Sergio & Vytlacil, Edward, 2006.
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2320, Institute for the Study of Labor (IZA).
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- James J. Heckman & Sergio Urzua & Edward Vytlacil, 2009. "Understanding Instrumental Variables in Models with Essential Heterogeneity," Working Papers 200941, Geary Institute, University College Dublin.
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- Gel, Yulia R. & Gastwirth, Joseph L., 2008. "A robust modification of the Jarque-Bera test of normality," Economics Letters, Elsevier, vol. 99(1), pages 30-32, April.
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- Deb, Partha & Sefton, Martin, 1996. "The distribution of a Lagrange multiplier test of normality," Economics Letters, Elsevier, vol. 51(2), pages 123-130, May.
- Shalit, Haim & Yitzhaki, Shlomo, 2002. " Estimating Beta," Review of Quantitative Finance and Accounting, Springer, vol. 18(2), pages 95-118, March.
- Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 90(3), pages 304-309, March.
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