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multiple testing

Author

Listed:
  • Joseph P. Romano
  • Azeem M. Shaikh
  • Michael Wolf

Abstract

Multiple testing refers to any instance that involves the simultaneous testing of more than one hypothesis. If decisions about the individual hypotheses are based on the unadjusted marginal p-values, then there is typically a large probability that some of the true null hypotheses will be rejected. Unfortunately, such a course of action is still common. In this article, we describe the problem of multiple testing more formally and discuss methods which account for the multiplicity issue. In particular, recent developments based on resampling result in an improved ability to reject false hypotheses compared to classical methods such as Bonferroni.

Suggested Citation

  • Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "multiple testing," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    • Handle: RePEc:pal:dofeco:v:4:year:2010:doi:3826
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    Citations

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    Cited by:

    1. Cushman, David O. & De Vita, Glauco, 2017. "Exchange rate regimes and FDI in developing countries: A propensity score matching approach," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 143-163.
    2. Tania Singer & Ernst Fehr, 2005. "The Neuroeconomics of Mind Reading and Empathy," American Economic Review, American Economic Association, vol. 95(2), pages 340-345, May.
    3. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    4. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 417-442, November.
    5. Goldman, Matt & Kaplan, David M., 2018. "Comparing distributions by multiple testing across quantiles or CDF values," Journal of Econometrics, Elsevier, vol. 206(1), pages 143-166.
    6. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
    7. Sneha Elango & Jorge Luis García & James J. Heckman & Andrés Hojman, 2015. "Early Childhood Education," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 235-297, National Bureau of Economic Research, Inc.
    8. Armin Falk & Ernst Fehr & Christian Zehnder, "undated". "The Behavioral Effects of Minimum Wages," IEW - Working Papers 247, Institute for Empirical Research in Economics - University of Zurich.
    9. David Afshartous & Michael Wolf, 2007. "Avoiding ‘data snooping’ in multilevel and mixed effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1035-1059, October.
    10. Joseph P. Romano & Michael Wolf, 2008. "Balanced Control of Generalized Error Rates," IEW - Working Papers 379, Institute for Empirical Research in Economics - University of Zurich.
    11. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.

    More about this item

    Keywords

    Multiple Testing; Familywise Error Rate; real estate finance; Resampling;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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