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Inference on winners

Author: Isaiah Smith Andrews; Toru Kitagawa; Adam McCloskey; National Bureau of Economic Research,
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2019.
Series: Working paper series (National Bureau of Economic Research), no. 25456.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Many empirical questions 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 confidence intervals are unreliable.  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Isaiah Smith Andrews; Toru Kitagawa; Adam McCloskey; National Bureau of Economic Research,
OCLC Number: 1084478841
Notes: "January 2019"
Includes online appendix (pages 51-126).
Description: 1 online resource (50 pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 25456.
Responsibility: Isaiah Andrews, Toru Kitagawa, Adam McCloskey.

Abstract:

Many empirical questions 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 confidence intervals are unreliable. This paper develops optimal confidence 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 confidence sets to offer further performance gains relative to existing alternatives.

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