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Sensitivity of the chi-squared goodness-of-fit test to the partitioning of data

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  • Boero, Gianna
  • Smith, Jeremy
  • Wallis, Kenneth F.

Abstract

In 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.

Suggested Citation

  • Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2004. "Sensitivity of the chi-squared goodness-of-fit test to the partitioning of data," Economic Research Papers 269588, University of Warwick - Department of Economics.
    • Handle: RePEc:ags:uwarer:269588
    DOI: 10.22004/ag.econ.269588
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    References listed on IDEAS

    as
    1. Marrocu, Emanuela & Gianna Boero, 2003. "The Performance of SETAR models by Regime: A Conditional Evaluation of Interval and Density Forecasts," Royal Economic Society Annual Conference 2003 147, Royal Economic Society.
    2. Boero, Gianna & Marrocu, Emanuela, 2004. "The performance of SETAR models: a regime conditional evaluation of point, interval and density forecasts," International Journal of Forecasting, Elsevier, vol. 20(2), pages 305-320.
    3. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2004. "Decompositions of Pearson's chi-squared test," Journal of Econometrics, Elsevier, vol. 123(1), pages 189-193, November.
    4. Benoit Mandelbrot, 1963. "New Methods in Statistical Economics," Journal of Political Economy, University of Chicago Press, vol. 71(5), pages 421-421.
    5. repec:sae:niesru:v:167:y::i:1:p:106-112 is not listed on IDEAS
    6. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
    7. Anderson, Gordon, 1996. "Nonparametric Tests of Stochastic Dominance in Income Distributions," Econometrica, Econometric Society, vol. 64(5), pages 1183-1193, September.
    8. Kenneth F. Wallis, 1999. "Asymmetric density forecasts of inflation and the Bank of England's fan chart," National Institute Economic Review, National Institute of Economic and Social Research, vol. 167(1), pages 106-112, January.
    9. Anderson, Gordon, 1994. "Simple tests of distributional form," Journal of Econometrics, Elsevier, vol. 62(2), pages 265-276, June.
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    Cited by:

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    2. Bontemps, Christian, 2013. "Moment-Based Tests for Discrete Distributions," IDEI Working Papers 772, Institut d'Économie Industrielle (IDEI), Toulouse, revised Oct 2014.
    3. Oller, Lars-Erik & Teterukovsky, Alex, 2007. "Quantifying the quality of macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 23(2), pages 205-217.
    4. Gordon Anderson, 2008. "The empirical assessment of multidimensional welfare, inequality and poverty: Sample weighted multivariate generalizations of the Kolmogorov–Smirnov two sample tests for stochastic dominance," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 73-87, March.
    5. Bontemps, Christian, 2014. "Simple moment-based tests for value-at-risk models and discrete distribution," TSE Working Papers 14-535, Toulouse School of Economics (TSE).
    6. Hasebe, Takuya & Vijverberg, Wim P., 2012. "A Flexible Sample Selection Model: A GTL-Copula Approach," IZA Discussion Papers 7003, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Agricultural and Food Policy; Research Methods/ Statistical Methods;

    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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