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Hypothesis Testing in Econometrics

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Author Info

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

    ()
    (Departments of Economics and Statistics, Stanford University, Stanford, California 94305
    Department of Economics, University of Chicago, Chicago, Illinois 60637
    Institute for Empirical Research in Economics, University of Z�rich, CH-8006 Z�rich, Switzerland)

Abstract

This article reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, we summarize some of the most important methods, as well as resampling methodology, which is useful to set critical values. Finally, we consider the problem of multiple testing, which has witnessed a burgeoning literature in recent years. Along the way, we incorporate some examples that are current in the econometrics literature. While many problems with well-known successful solutions are included, we also address open problems that are not easily handled with current technology, stemming from such issues as lack of optimality or poor asymptotic approximations.

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File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev.economics.102308.124342
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Bibliographic Info

Article provided by Annual Reviews in its journal Annual Review of Economics.

Volume (Year): 2 (2010)
Issue (Month): 1 (09)
Pages: 75-104

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Handle: RePEc:anr:reveco:v:2:y:2010:p:75-104

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Related research

Keywords: asymptotics; multiple testing; optimality; resampling;

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References

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  1. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
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  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, vol. 17(3), pages 417-442, November.
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Citations

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Cited by:
  1. Doyle, O.; & Harmon, C.; & Heckman, J.J.; & Logue, C,; & Moon, S.H.;, 2013. "Measuring Investment in Human Capital Formation: An Experimental Analysis of Early Life Outcomes," Health, Econometrics and Data Group (HEDG) Working Papers 13/18, HEDG, c/o Department of Economics, University of York.
  2. Shen, Yanfeng & Lin, Zhengyan & Zhu, Jun, 2011. "Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2221-2233, July.
  3. Joachim Freyberger & Joel Horowitz, 2012. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers CWP15/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Meta Brown & Wilbert van der Klaauw & Jaya Wen & Basit Zafar, 2013. "Financial education and the debt behavior of the young," Staff Reports 634, Federal Reserve Bank of New York.
  5. Joachim Freyberger & Joel Horowitz, 2013. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers CWP31/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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