The Sensitivity of Chi-Squared Goodness-of-Fit Tests to the Partitioning of Data

Gianna Boero (), Jeremy Smith and Kenneth Wallis ()

Econometric Reviews, 2005, vol. 23, issue 4, 341-370

Abstract: The power of Pearson's overall goodness-of-fit test and the components-of-chi-squared or “Pearson analog” tests of Anderson [Anderson, G. (1994). Simple tests of distributional form. J. Econometrics 62:265-276] to detect rejections due to shifts in location, scale, skewness and kurtosis is studied, as the number and position of the partition points is varied. Simulations are conducted for small and moderate sample sizes. It is found that smaller numbers of classes than are used in practice may be appropriate, and that the choice of non-equiprobable classes can result in substantial gains in power.

Keywords: Pearson's goodness-of-fit test; Component tests; Monte Carlo; Number of classes; Partitions (search for similar items in EconPapers)
Date: 2005
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Related works:
Working Paper: Sensitivity of the chi-squared goodness-of-fit test to the partitioning of data (2004)
Working Paper: Sensitivity of the Chi-Squared Goodness-of-Fit Test to the Partitioning of Data (2004)
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DOI: 10.1081/ETC-200040782

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