The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES
Open accessAbstract
It is common in econometric applications that several hypothesis tests are carried out simultaneously. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. The classical approach is to control the familywise error rate (FWE), which is the probability of one or more false rejections. But when the number of hypotheses under consideration is large, control of the FWE can become too demanding. As a result, the number of false hypotheses rejected may be small or even zero. This suggests replacing control of the FWE by a more liberal measure. To this end, we review a number of recent proposals from the statistical literature. We briefly discuss how these procedures apply to the general problem of model selection. A simulation study and two empirical applications illustrate the methods.We thank three anonymous referees for helpful comments that have led to an improved presentation of the paper. The research of the third author has been partially supported by the Spanish Ministry of Science and Technology and FEDER, Grant BMF2003-03324.
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Cites
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
A Test for Superior Predictive Ability
Hansen, P. R. (2005) · Journal of Business & Economic Statistics · doi:10.1198/073500105000000063 · Cited by 1,230
Data-snooping, technical trading rule performance and the Bootstrap
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Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing
Romano, J. P., & Wolf, M. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000000539 · Cited by 609
Self-citationGeneralizations of the familywise error rate
Lehmann, E. L., & Romano, J. P. (2005) · The Annals of Statistics · doi:10.1214/009053605000000084 · Cited by 320
Self-citation
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Harvey, C. R., Liu, Y., & Zhu, H. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhv059 · Cited by 2,119
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Robust performance hypothesis testing with the Sharpe ratio
Ledoit, O., & Wolf, M. (2008) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2008.03.002 · Cited by 948
Self-citationFalse Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas
BARRAS, L., SCAILLET, O., & WERMERS, R. (2010) · The Journal of Finance · doi:10.1111/j.1540-6261.2009.01527.x · Cited by 825
One Swallow Doesn't Make a Summer: New Evidence on Anchoring Effects
Maniadis, Z., Tufano, F., & List, J. A. (2014) · American Economic Review · doi:10.1257/aer.104.1.277 · Cited by 280
Metadata
- Authors
- Romano, J. P., Shaikh, A. M., & Wolf, M.
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 2007-11-30
- Volume
- 24
- Issue
- 02
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W1967298524
- Cited by
- 185
- References
- 5
- FWCI
- 10.21
- Type
- journal article
- Language
- en
- Open access
- green
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