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Hypothesis Testing for Arbitrary Bounds

Author

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  • Jeffrey Penney

    () (Queen's University)

Abstract

I derive a rigorous method to help determine whether a true parameter takes a value between two arbitrarily chosen points for a given level of confidence via a multiple testing procedure which strongly controls the familywise error rate. For any test size, the distance between the upper and lower bounds can be made smaller than that created by a confidence interval. The procedure is more powerful than other multiple testing methods that test the same hypothesis. This test can be used to provide an affirmative answer about the existence of a negligible effect.

Suggested Citation

  • Jeffrey Penney, 2013. "Hypothesis Testing for Arbitrary Bounds," Working Papers 1319, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1319
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1319.pdf
    File Function: First version 2013
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    References listed on IDEAS

    as
    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2009. "Optimal testing of multiple hypotheses with common effect direction," Biometrika, Biometrika Trust, vol. 96(2), pages 399-410.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    familywise error; multiple testing; null effect; partial identification; precise zero;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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