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Location Characteristics of Conditional Selective Confidence Intervals via Polyhedral Methods

Author

Listed:
  • Dzemski, Andreas

    (Department of Economics, School of Business, Economics and Law, Göteborg University)

  • Okui, Ryo

    (Faculty of Economics, the University of Tokyo)

  • Wang, Wenjie

    (Division of Economics, School of Social Sciences, Nanyang Technological University)

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

  • Dzemski, Andreas & Okui, Ryo & Wang, Wenjie, 2025. "Location Characteristics of Conditional Selective Confidence Intervals via Polyhedral Methods," Working Papers in Economics 851, University of Gothenburg, Department of Economics.
    • Handle: RePEc:hhs:gunwpe:0851
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    More about this item

    Keywords

    Selective inference; statistical significance; confidence interval; polyhedral method; Multiple hypothesis testing; post-selection inference; conditional inference.;
    All these keywords.

    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

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