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Benchmarking historical corporate performance


  • Scott, James G.


This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for making cross-peer-group comparisons among companies in very different industries and operating environments. This is done by using models for Bayesian multiple hypothesis testing to determine which firms, if any, have systematically out-performed their peer groups over time. We conclude that systematic out-performance, while it seems to exist, is quite rare worldwide.

Suggested Citation

  • Scott, James G., 2012. "Benchmarking historical corporate performance," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1795-1807.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1795-1807 DOI: 10.1016/j.csda.2011.11.004

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    References listed on IDEAS

    1. Kim-Anh Do & Peter Müller & Feng Tang, 2005. "A Bayesian mixture model for differential gene expression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 627-644.
    2. Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    3. Dahl, David B. & Newton, Michael A., 2007. "Multiple Hypothesis Testing by Clustering Treatment Effects," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 517-526, June.
    4. Gabriel Hawawini & Venkata Subban Subramanian & Paul Verdin, 2003. "Is performance driven by industry- or firm-specific factors? A new look at the evidence," ULB Institutional Repository 2013/14188, ULB -- Universite Libre de Bruxelles.
    5. Gramacy, Robert B & Lee, Herbert K. H, 2008. "Bayesian Treed Gaussian Process Models With an Application to Computer Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1119-1130.
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