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New Graphical Methods and Test Statistics for Testing Composite Normality

Marc S. Paolella ()
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Marc S. Paolella: Department of Banking and Finance, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland

Econometrics, 2015, vol. 3, issue 3, 1-29

Abstract: Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one test, called the modified stabilized probability test, or MSP, a highly simplified computational method is derived, which delivers the test statistic and also a highly accurate p-value approximation, essentially instantaneously. The MSP test is demonstrated to have higher power against asymmetric alternatives than the well-known and powerful Jarque-Bera test. A further size-correct test, based on combining two test statistics, is shown to have yet higher power. The methodology employed is fully general and can be applied to any i.i.d. univariate continuous distribution setting.

Keywords: calibration for simultaneity; combined tests; distribution testing; P-P plot; Q-Q plot; simultaneous null bands (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2015
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