IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.20917.html
   My bibliography  Save this paper

Location Characteristics of Conditional Selective Confidence Intervals via Polyhedral Methods

Author

Listed:
  • Andreas Dzemski
  • Ryo Okui
  • Wenjie Wang

Abstract

We examine the location characteristics of a conditional selective confidence interval based on the polyhedral method. This interval is constructed from the distribution of a test statistic conditional upon the event of statistical significance. In the case of a one-sided test, the behavior of the interval varies depending on whether the parameter is highly significant or only marginally significant. When the parameter is highly significant, the interval is similar to the usual confidence interval derived without considering selection. However, when the parameter is only marginally significant, the interval falls into an extreme range and deviates greatly from the estimated value of the parameter. In contrast, an interval conditional on two-sided significance does not yield extreme results, although it may exclude the estimated parameter value.

Suggested Citation

  • Andreas Dzemski & Ryo Okui & Wenjie Wang, 2025. "Location Characteristics of Conditional Selective Confidence Intervals via Polyhedral Methods," Papers 2502.20917, arXiv.org.
    • Handle: RePEc:arx:papers:2502.20917
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.20917
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    2. Danijel Kivaranovic & Hannes Leeb, 2021. "On the Length of Post-Model-Selection Confidence Intervals Conditional on Polyhedral Constraints," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 845-857, April.
    3. Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
    4. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
    5. Gerber, Alan & Malhotra, Neil, 2008. "Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals," Quarterly Journal of Political Science, now publishers, vol. 3(3), pages 313-326, October.
    6. Yoshikazu Terada & Hidetoshi Shimodaira, 2023. "Selective inference after feature selection via multiscale bootstrap," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 99-125, February.
    7. Ryan J. Tibshirani & Jonathan Taylor & Richard Lockhart & Robert Tibshirani, 2016. "Exact Post-Selection Inference for Sequential Regression Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 600-620, April.
    8. Vo Nguyen Le Duy & Ichiro Takeuchi, 2023. "Exact statistical inference for the Wasserstein distance by selective inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 127-157, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uwe Hassler & Marc‐Oliver Pohle, 2022. "Unlucky Number 13? Manipulating Evidence Subject to Snooping," International Statistical Review, International Statistical Institute, vol. 90(2), pages 397-410, August.
    2. Graham Elliott & Nikolay Kudrin & Kaspar Wüthrich, 2022. "Detecting p‐Hacking," Econometrica, Econometric Society, vol. 90(2), pages 887-906, March.
    3. Alexander L. Brown & Taisuke Imai & Ferdinand M. Vieider & Colin F. Camerer, 2024. "Meta-analysis of Empirical Estimates of Loss Aversion," Journal of Economic Literature, American Economic Association, vol. 62(2), pages 485-516, June.
    4. Jelle J Goeman & Aldo Solari, 2024. "On selection and conditioning in multiple testing and selective inference," Biometrika, Biometrika Trust, vol. 111(2), pages 393-416.
    5. Brodeur, Abel & Esterling, Kevin & Ankel-Peters, Jörg & Bueno, Natália S & Desposato, Scott & Dreber, Anna & Genovese, Federica & Green, Donald P & Hepplewhite, Matthew & de la Guardia, Fernando Hoces, 2024. "Promoting Reproducibility and Replicability in Political Science," Department of Economics, Working Paper Series qt23n3n3dg, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    6. Anna Dreber & Magnus Johannesson & Yifan Yang, 2024. "Selective reporting of placebo tests in top economics journals," Economic Inquiry, Western Economic Association International, vol. 62(3), pages 921-932, July.
    7. Ehrenbergerova, Dominika & Bajzik, Josef & Havranek, Tomas, 2021. "When Does Monetary Policy Sway House Prices? A Meta-Analysis," MetaArXiv npeqs_v1, Center for Open Science.
    8. Felix Chopra & Ingar Haaland & Christopher Roth & Andreas Stegmann, 2024. "The Null Result Penalty," The Economic Journal, Royal Economic Society, vol. 134(657), pages 193-219.
    9. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "The Power of Tests for Detecting $p$-Hacking," Papers 2205.07950, arXiv.org, revised Apr 2024.
    10. Abel Brodeur & Nikolai Cook & Carina Neisser, 2024. "p-Hacking, Data type and Data-Sharing Policy," The Economic Journal, Royal Economic Society, vol. 134(659), pages 985-1018.
    11. Dominika Ehrenbergerova & Josef Bajzik & Tomas Havranek, 2023. "When Does Monetary Policy Sway House Prices? A Meta-Analysis," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 538-573, June.
    12. Brodeur, Abel & Cook, Nikolai & Hartley, Jonathan & Heyes, Anthony, 2022. "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," MetaArXiv uxf39, Center for Open Science.
    13. Stefano DellaVigna & Elizabeth Linos, 2022. "RCTs to Scale: Comprehensive Evidence From Two Nudge Units," Econometrica, Econometric Society, vol. 90(1), pages 81-116, January.
    14. Bruns, Stephan & Herwartz, Helmut & Ioannidis, John P.A. & Islam, Chris-Gabriel & Raters, Fabian H. C., 2023. "Statistical reporting errors in economics," MetaArXiv mbx62, Center for Open Science.
    15. Jindrich Matousek & Tomas Havranek & Zuzana Irsova, 2022. "Individual discount rates: a meta-analysis of experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 318-358, February.
    16. Christoph Huber & Christian König-Kersting & Matteo M. Marini, 2022. "Experimenting with Financial Professionals," Working Papers 2022-07, Faculty of Economics and Statistics, Universität Innsbruck, revised Jun 2024.
    17. Abel Brodeur, Nikolai M. Cook, Anthony Heyes, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," LCERPA Working Papers am0133, Laurier Centre for Economic Research and Policy Analysis.
    18. Sebastian Gechert & Tomas Havranek & Zuzana Irsova & Dominika Kolcunova, 2022. "Measuring Capital-Labor Substitution: The Importance of Method Choices and Publication Bias," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 55-82, July.
    19. Adam Gorajek & Joel Bank & Andrew Staib & Benjamin Malin & Hamish Fitchett, 2021. "Star Wars at Central Banks," RBA Research Discussion Papers rdp2021-02, Reserve Bank of Australia.
    20. Jasper Brinkerink, 2023. "When Shooting for the Stars Becomes Aiming for Asterisks: P-Hacking in Family Business Research," Entrepreneurship Theory and Practice, , vol. 47(2), pages 304-343, March.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2502.20917. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.