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Hypothesis Testing in Econometrics

Author

Listed:
  • 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 Zürich, CH-8006 Zürich, 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.

Suggested Citation

  • Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    • Handle: RePEc:anr:reveco:v:2:y:2010:p:75-104
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev.economics.102308.124342
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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