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Goodness of Fit: An Axiomatic Approach

Author

Listed:
  • Frank A. Cowell

    (STICERD - LSE - London School of Economics and Political Science)

  • Russell Davidson

    (Department of Mining and Materials Engineering [Montréal] - McGill University = Université McGill [Montréal, Canada], GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Emmanuel Flachaire

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

An axiomatic approach is used to develop a one-parameter family of measures of divergence between distributions. These measures can be used to perform goodness-of-fit tests with good statistical properties. Asymptotic theory shows that the test statistics have well-defined limiting distributions which are, however, analytically intractable. A parametric bootstrap procedure is proposed for implementation of the tests. The procedure is shown to work very well in a set of simulation experiments, and to compare favorably with other commonly used goodness-of-fit tests. By varying the parameter of the statistic, one can obtain information on how the distribution that generated a sample diverges from the target family of distributions when the true distribution does not belong to that family. An empirical application analyzes a U.K. income dataset.

Suggested Citation

  • Frank A. Cowell & Russell Davidson & Emmanuel Flachaire, 2015. "Goodness of Fit: An Axiomatic Approach," Post-Print hal-01456107, HAL.
    • Handle: RePEc:hal:journl:hal-01456107
    DOI: 10.1080/07350015.2014.922470
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    Cited by:

    1. Denis Bouyssou & Thierry Marchant & Marc Pirlot, 2021. "Axiomatic characterization of the χ 2 dissimilarity measure," Working Papers hal-03463741, HAL.
    2. Frank Cowell & Emmanuel Flachaire & Sanghamitra Bandyopadhyay, 2013. "Reference distributions and inequality measurement," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(4), pages 421-437, December.
    3. Tsvetana Spasova, 2019. "Regional Income Distribution in the European Union: A Parametric Approach," Research on Economic Inequality, in: What Drives Inequality?, volume 27, pages 1-18, Emerald Group Publishing Limited.
    4. Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.

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