Sensitivity of the Chi-Squared Goodness-of-Fit Test to the Partitioning of Data
AbstractIn this paper we conduct a Monte Carlo study to determine the power of Pearson’s overall goodness-of-fit test as well as the “Pearson analog” tests (see Anderson (1994)) to detect rejections due to shifts in variance, skewness and kurtosis, as we vary the number and location of the partition points. Simulations are conducted for small and moderate sample sizes. While it is generally recommended that to improve the power of the goodness-of-fit test the partition points are equiprobable, we find that power can be improved by the use of non-equiprobable partitions.
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Bibliographic InfoPaper provided by University of Warwick, Department of Economics in its series The Warwick Economics Research Paper Series (TWERPS) with number 694.
Length: 34 pages
Date of creation: 2004
Date of revision:
Pearson’s Goodness-of-fit test ; Distributional assumptions ; Monte Carlo ; Normality ; partitions;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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