Extended Neyman smooth goodness-of-fit tests, applied to competing heavy-tailed distributions

J. Huston McCulloch and E. Richard Percy

Journal of Econometrics, 2013, vol. 172, issue 2, 275-282

Abstract: A simplified version of the Neyman (1937) “Smooth” goodness-of-fit test is extended to account for the presence of estimated model parameters, thereby removing overfitting bias. Using a Lagrange Multiplier approach rather than the Likelihood Ratio statistic proposed by Neyman greatly simplifies the calculations. Polynomials, splines, and the step function of Pearson’s test are compared as alternative perturbations to the theoretical uniform distribution. The extended tests have negligible size distortion and more power than standard tests. The tests are applied to competing symmetric leptokurtic distributions with US stock return data. These are generally rejected, primarily because of the presence of skewness.

Keywords: Stable distribution; Student t distribution; Generalized error distribution; Lagrange multiplier test (search for similar items in EconPapers)
JEL-codes: C12 C16 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:172:y:2013:i:2:p:275-282

DOI: 10.1016/j.jeconom.2012.08.018

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