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Inference on winners

Author

Listed:
  • Isaiah Andrews

    (Institute for Fiscal Studies and Harvard University)

  • Toru Kitagawa

    (Institute for Fiscal Studies and University College London)

  • Adam McCloskey

    (Institute for Fiscal Studies and Brown University)

Abstract

Many questions in econometrics can be cast as inference on a parameter selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner's curse, where conventional estimates are biased and conventional confi dence intervals are unreliable. This paper develops optimal con fidence sets and median-unbiased estimators that are valid conditional on the parameter selected and so overcome this winner's curse. If one requires validity only on average over target parameters that might have been selected, we develop hybrid procedures that combine conditional and projection con fidence sets and offer further performance gains that are attractive relative to existing alternatives.

Suggested Citation

  • Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP31/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:31/18
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    Cited by:

    1. Magne Mogstad & Joseph P Romano & Azeem M Shaikh & Daniel Wilhelm, 2024. "Inference for Ranks with Applications to Mobility across Neighbourhoods and Academic Achievement across Countries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(1), pages 476-518.
    2. Will Davis & Alexander Gordan & Rusty Tchernis, 2021. "Measuring the spatial distribution of health rankings in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2921-2936, November.
    3. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayes Selection for Value Maximization," Papers 2210.03905, arXiv.org, revised Jan 2023.
    4. Sergei Bazylik & Magne Mogstad & Joseph P. Romano & Azeem Shaikh & Daniel Wilhelm, 2021. "Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties," NBER Working Papers 29519, National Bureau of Economic Research, Inc.
    5. Andrews, Isaiah & Kitagawa, Toru & McCloskey, Adam, 2021. "Inference after estimation of breaks," Journal of Econometrics, Elsevier, vol. 224(1), pages 39-59.
    6. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
    7. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2024.
    8. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    9. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2023. "Inference for Linear Conditional Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(6), pages 2763-2791.
    10. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    11. Hsieh, Yu-Wei & Shi, Xiaoxia & Shum, Matthew, 2022. "Inference on estimators defined by mathematical programming," Journal of Econometrics, Elsevier, vol. 226(2), pages 248-268.
    12. David J. Deming, 2021. "The Growing Importance of Decision-Making on the Job," NBER Working Papers 28733, National Bureau of Economic Research, Inc.
    13. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
    14. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Oct 2023.

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    More about this item

    Keywords

    Winner's Curse; Selective Inference;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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