Inference on Winners*.

Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner's curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner's curse biases...

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Publicado en:Quarterly Journal of Economics Vol. 139; no. 1; pp. 305 - 359
Autores principales: Andrews, Isaiah, Kitagawa, Toru, McCloskey, Adam
Formato: Artículo
Publicado: Oxford University Press / USA Feb2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2024
      vid: 139
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      pub: Oxford University Press / USA
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        atl: Inference on Winners*.
      aug:
        au:
          Andrews, Isaiah
          Kitagawa, Toru
          McCloskey, Adam
        affil:
          Massachusetts Institute of Technology , United States
          Brown University , United States
          University of Colorado at Boulder , United States
      su:
        Neighborhoods
        Sampling errors
        Economic opportunities
        Confidence intervals
        Research personnel
      sug:
        subj:
          Neighborhoods
          Sampling errors
          Economic opportunities
          Confidence intervals
          Research personnel
      ab: Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner's curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner's curse biases our estimates for selected options upward and can invalidate conventional confidence intervals. This article develops estimators and confidence intervals that eliminate this winner's curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner's curse corrections can make an economically significant difference to conclusions but still allow informative inference.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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