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A K-sample Homogeneity Test based on the Quantification of the p-p Plot

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Author Info

  • Jeroen Hinloopen

    ()
    (University of Amsterdam)

  • Rien Wagenvoort

    ()
    (European Investment Bank, Luxemburg)

  • Charles van Marrewijk

    ()
    (Erasmus University Rotterdam)

Abstract

We propose a quantification of the p-p plot that assigns equal weight to all distances between the respective distributions: the surface between the p-p plot and the diagonal. This surface is labelled the Harmonic Weighted Mass (HWM) index. We introduce the diagonal-deviation (d-d) plot that allows the index to be computed exactly under all circumstances. For two balanced samples absent ties the finite sample distribution of the HWM index is derived. Simulations show that in most cases unbalanced samples and ties have little effect on this distribution. The d-d plot allows for a straightforward extension to the K-sample HWM index. As we have not been able to derive the distribution of the index for K>2, we simulate significance tables for K=3,...,15. An example involving economic growth rates of the G7 countries illustrates that the HWM test can have better power than alternative Empirical Distribution Function tests.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-100/1.

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Date of creation: 20 Oct 2008
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Handle: RePEc:dgr:uvatin:20080100

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Web page: http://www.tinbergen.nl

Related research

Keywords: EDF test; p-p plot; power; d-d plot;

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References

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  1. Jean-Marie Dufour, 2005. "Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and non-standard asymptotics," CIRANO Working Papers 2005s-02, CIRANO.
  2. Dufour, J.M. & Kiviet, J.F., 1995. "Exact Inference Methods for First-Order Autoregressive Distributed Lag Models," Cahiers de recherche 9547, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
  3. Schmid, Friedrich & Trede, Mark, 1995. "A distribution free test for the two sample problem for general alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 20(4), pages 409-419, October.
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Cited by:
  1. Steven Brakman & Robert Inklaar & Charles van Marrewijk, 2010. "Structural Change in OECD Comparative Advantage," CESifo Working Paper Series 3033, CESifo Group Munich.

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