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Hypothesis Testing in Econometrics

  • 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)

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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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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  1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
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  8. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(02), pages 404-447, April.
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  16. Joseph P. Romano & Michael Wolf, 2005. "Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 94-108, March.
  17. 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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  25. repec:cup:cbooks:9780521496032 is not listed on IDEAS
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