Abstract

http://ssrn.com/abstract=2111936
 
 

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Using OLS to Test for Normality


Haim Shalit


Ben-Gurion University of the Negev - Department of Economics

July 14, 2012


Abstract:     
Yitzhaki (1996) showed that the OLS estimator of the slope coefficient in a simple regression is a weighted average of the slopes delineated by adjacent observations. The weights depend only on the distribution of the independent variable. In this paper I demonstrate that equal weights can only be obtained if and only if the independent variable is normally distributed. This necessary and sufficient condition is used to develop a new test for normality which is distribution free and not sensitive to outliers. The test is compared with standard normality tests, in particular, the popular Jarque-Bera test. It is shown that the new test provides a better power for testing normality against all classes of alternative distributions. Finally, the test is applied to check normality in time-series data from major international financial markets.

Number of Pages in PDF File: 16

Keywords: regression weights, Jarque-Bera test, Kolmogorov-Smirnov test

JEL Classification: C10

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Date posted: July 19, 2012  

Suggested Citation

Shalit, Haim, Using OLS to Test for Normality (July 14, 2012). Available at SSRN: http://ssrn.com/abstract=2111936 or http://dx.doi.org/10.2139/ssrn.2111936

Contact Information

Haim Shalit (Contact Author)
Ben-Gurion University of the Negev - Department of Economics ( email )
Department of Economics
Beer-Sheva 84105
Israel
+972-8-6472299 (Phone)
+972-8-6472941 (Fax)
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