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Hypothesis Testing For Arbitrary Bounds

Author

Listed:
  • 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 Paper 1319, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1319
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    File URL: https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1319.pdf
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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.
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    Cited by:

    1. Vincent Chandler & Lealand Morin & Jeffrey Penney, 2023. "Penalties for speeding and their effect on moving violations: Evidence from Quebec drivers," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(1), pages 225-246, February.
    2. Jeffrey Penney, 2018. "Dynamic Treatment Effects Of Teacher'S Aides In An Experiment With Multiple Randomizations," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1244-1260, April.

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    More about this item

    Keywords

    familywise error; multiple testing; null effect; partial identification; precise zero;
    All these keywords.

    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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