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Stepwise Multiple Testing as Formalized Data Snooping

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Author Info
Joseph P. Romano
Michael Wolf
Abstract

In econometric applications, often several hypothesis tests are carried out at once. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure that asymptotically controls the familywise error rate. Compared to related single-step methods, the procedure is more powerful and often will reject more false hypotheses. In addition, we advocate the use of studentization when feasible. Unlike some stepwise methods, the method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses. The methodology is presented in the context of comparing several strategies to a common benchmark. However, our ideas can easily be extended to other contexts where multiple tests occur. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data. Copyright The Econometric Society 2005.

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File URL: http://hdl.handle.net/10.1111/j.1468-0262.2005.00615.x
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Publisher Info
Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 73 (2005)
Issue (Month): 4 (07)
Pages: 1237-1282
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:ecm:emetrp:v:73:y:2005:i:4:p:1237-1282

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  1. Xiaohong Chen & Yanqin Fan & Demian Pouzo & Zhiliang Ying, 2008. "Estimation and Model Selection of Semiparametric Multivariate Survival Functions under General Censorship," Cowles Foundation Discussion Papers 1683, Cowles Foundation, Yale University. [Downloadable!]
  2. Michael Wolf & Dan Wunderli, 2009. "Fund-of-funds construction by statistical multiple testing methods," IEW - Working Papers iewwp445, Institute for Empirical Research in Economics - IEW. [Downloadable!]
  3. Hanck, Christoph, 2008. "Now, whose schools are really better (or weaker) than Germany's? A multiple testing approach," MPRA Paper 12008, University Library of Munich, Germany. [Downloadable!]
  4. Gilles Daniel & Didier Sornette & Peter Wohrmann, 2008. "Look-Ahead Benchmark Bias in Portfolio Performance Evaluation," Quantitative Finance Papers 0810.1922, arXiv.org. [Downloadable!]
  5. Christopher J. Bennett, 2009. "p-Value Adjustments for Asymptotic Control of the Generalized Familywise Error Rate," Working Papers 0905, Department of Economics, Vanderbilt University. [Downloadable!]
  6. Christoph Hanck, 2009. "For which countries did PPP hold? A multiple testing approach," Empirical Economics, Springer, vol. 37(1), pages 93-103, September. [Downloadable!] (restricted)
  7. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008. [Downloadable!]
  8. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104. [Downloadable!]
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  9. Joseph P. Romano & Michael Wolf, . "Control of Generalized Error Rates in Multiple Testing," IEW - Working Papers iewwp245, Institute for Empirical Research in Economics - IEW. [Downloadable!]
  10. David Afshartous & Michael Wolf, 2005. "Avoiding Data Snooping in Multilevel and Mixed Effects Models," IEW - Working Papers iewwp260, Institute for Empirical Research in Economics - IEW. [Downloadable!]
  11. Joseph P & Romano & Azeem M. Shaikh & Michael Wolf, 2005. "Formalized Data Snooping Based on Generalized Error Rates," IEW - Working Papers iewwp259, Institute for Empirical Research in Economics - IEW. [Downloadable!]
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