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Hypothesis testing in econometrics

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Author Info
Joseph P. Romano
Azeem M. Shaikh
Michael Wolf

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Abstract

This paper 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, some of the most important methods are summarized, 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 we include many problems with wellknown successful solutions, we also include open problems that are not easily handled with current technology, stemming from issues like lack of optimality or poor asymptotic approximations.

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Paper provided by Institute for Empirical Research in Economics - IEW in its series IEW - Working Papers with number iewwp444.

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Date of creation: Sep 2009
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Handle: RePEc:zur:iewwpx:444

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Related research
Keywords: Asymptotics; multiple testing; optimality; resampling;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing

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References listed on IDEAS
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  1. Andrews, Donald W. K., 1998. "Hypothesis testing with a restricted parameter space," Journal of Econometrics, Elsevier, vol. 84(1), pages 155-199, May. [Downloadable!] (restricted)
    Other versions:
  2. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May. [Downloadable!] (restricted)
    Other versions:
  3. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Oxford University Press for Biometrika Trust, vol. 93(3), pages 491-507, September. [Downloadable!] (restricted)
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  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(02), pages 404-447, April. [Downloadable!]
    Other versions:
  7. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205. [Downloadable!] (restricted)
  8. Michael Jansson, 2008. "Semiparametric Power Envelopes for Tests of the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 76(5), pages 1103-1142, 09. [Downloadable!] (restricted)
    Other versions:
  9. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July. [Downloadable!] (restricted)
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  10. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, 09. [Downloadable!] (restricted)
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  13. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2008. "Control of the False Discovery Rate under Dependence using the Bootstrap and Subsampling," IEW - Working Papers iewwp337, Institute for Empirical Research in Economics - IEW. [Downloadable!]
  14. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October. [Downloadable!] (restricted)
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  20. Anna Mikusheva, 2007. "Uniform Inference in Autoregressive Models," Econometrica, Econometric Society, vol. 75(5), pages 1411-1452, 09. [Downloadable!] (restricted)
  21. 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. [Downloadable!] (restricted)
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