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Simulating power of economic experiments: the powerBBK package

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  • Charles Bellemare

    (Laval University)

  • Luc Bissonnette

    (Laval University)

  • Sabine Kröger

    (Laval University)

Abstract

In this article, we highlight how simulation methods can be used to analyze power of economic experiments. We provide the powerBBK package programmed for experimental economists, that can be used to perform simulations in STATA. Power can be simulated using a single command line for various statistical tests (nonparametric and parametric), estimation methods (linear, binary, and censored regression models), treatment variables (binary, continuous, time-invariant or time varying), sample sizes, experimental periods, and other design features (within or between-subjects design). The package can be used to predict minimum sample sizes required to reach a user-specific level of power, to maximize power of a design given the researcher supplied a budget constraint, or to compute power to detect a user-specified treatment order effect in within-subjects designs. The package can also be used to compute the probability of sign errors—the probability of rejecting the null hypothesis in the wrong direction as well as the share of rejections pointing in the wrong direction. The powerBBK package is provided as an .ado file along with a help file, both of which can be downloaded here ( http://www.bbktools.org ).

Suggested Citation

  • Charles Bellemare & Luc Bissonnette & Sabine Kröger, 2016. "Simulating power of economic experiments: the powerBBK package," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 2(2), pages 157-168, November.
  • Handle: RePEc:spr:jesaex:v:2:y:2016:i:2:d:10.1007_s40881-016-0028-4
    DOI: 10.1007/s40881-016-0028-4
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    References listed on IDEAS

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    1. John List & Sally Sadoff & Mathis Wagner, 2011. "So you want to run an experiment, now what? Some simple rules of thumb for optimal experimental design," Experimental Economics, Springer;Economic Science Association, vol. 14(4), pages 439-457, November.
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    3. Uri Gneezy & John A List, 2006. "Putting Behavioral Economics to Work: Testing for Gift Exchange in Labor Markets Using Field Experiments," Econometrica, Econometric Society, vol. 74(5), pages 1365-1384, September.
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    13. Cloos, Janis & Greiff, Matthias & Rusch, Hannes, 2021. "Editorial favoritism in the field of laboratory experimental economics (RM/20/014-revised-)," Research Memorandum 005, Maastricht University, Graduate School of Business and Economics (GSBE).
    14. Nickolas Gagnon & Riccardo D. Saulle & Henrik W. Zaunbrecher, 2021. "Decreasing Incomes Increase Selfishness," Papers 2107.02888, arXiv.org.
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    18. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," GREDEG Working Papers 2017-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
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    20. Nicolas Vallois & Dorian Jullien, 2018. "A history of statistical methods in experimental economics," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 25(6), pages 1455-1492, November.
    21. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    22. Sebastian Jobjörnsson & Henning Schaak & Oliver Musshoff & Tim Friede, 2023. "Improving the statistical power of economic experiments using adaptive designs," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 357-382, April.
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    24. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," Working Papers halshs-01651070, HAL.

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