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RWOLF: Stata module to calculate Romano-Wolf stepdown p-values for multiple hypothesis testing

Author

Listed:
  • Damian Clarke

    (Universidad de Santiago de Chile)

Programming Language

Stata

Abstract

rwolf calculates Romano and Wolf's (Econometrica 2005; JASA 2005) stepdown adjusted p-values to correct for multiple hypothesis testing. This program follows the algorithm described in Romano and Wolf (Statistics and Probability Letters 2016), and provides a p-value corresponding to each of a series of J independent variables when testing multiple hypotheses against a single dependent (or treatment) variable. The rwolf algorithm constructs a null distribution for each of the J hypothesis tests based on Studentized bootstrap replications of a subset of the tested variables. Full details of the procedure are described in Romano and Wolf (2016).

Suggested Citation

  • Damian Clarke, 2016. "RWOLF: Stata module to calculate Romano-Wolf stepdown p-values for multiple hypothesis testing," Statistical Software Components S458276, Boston College Department of Economics, revised 08 Jul 2020.
    • Handle: RePEc:boc:bocode:s458276
    Note: This module should be installed from within Stata by typing "ssc install rwolf". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/r/rwolf.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/r/rwolf.sthlp
    File Function: help file
    Download Restriction: no
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    Citations

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    Cited by:

    1. Giaccherini, Matilde & Kopinska, Joanna & Palma, Alessandro, 2021. "When particulate matter strikes cities: Social disparities and health costs of air pollution," Journal of Health Economics, Elsevier, vol. 78(C).
    2. Sandrine Mesplé-Somps & Björn Nilsson, 2020. "Role models and migration intentions," Working Papers DT/2020/13, DIAL (Développement, Institutions et Mondialisation).
    3. Damian Clarke & Joseph P. Romano & Michael Wolf, 2020. "The Romano–Wolf multiple-hypothesis correction in Stata," Stata Journal, StataCorp LP, vol. 20(4), pages 812-843, December.
    4. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    5. Palma, Alessandro & Petrunyk, Inna & Vuri, Daniela, 2019. "Air Pollution during Pregnancy and Birth Outcomes in Italy," IZA Discussion Papers 12467, Institute of Labor Economics (IZA).
    6. Damian Clarke & Gustavo Cortés Méndez & Diego Vergara Sepúlveda, 2020. "Growing together: assessing equity and efficiency in a prenatal health program," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 883-956, July.
    7. Bilge Erten & Pinar Keskin, 2020. "Breaking the Cycle? Education and the Intergenerational Transmission of Violence," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 252-268, May.

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