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A Bayesian Critic for Frequentist Procedures

Author

Listed:
  • Isaiah Andrews
  • Simon C. Essig Aberg
  • Jesse M. Shapiro

Abstract

We propose a method for automated, probabilistic evaluation of the frequentist properties (e.g., bias, coverage) of procedures (e.g., estimators, confidence intervals) in a given setting. A Bayesian critic observes a sample of data and updates their prior belief on the underlying data-generating process (DGP). The resulting posterior belief about the DGP implies a posterior belief about the property of interest. When the critic's prior is in a low-precision Dirichlet process class, the critic's posterior can be approximated via a Bayesian bootstrap, making the method fully automated. We apply the method to several canonical settings and show that the critic shares some concerns raised in previous work and delivers new insights.

Suggested Citation

  • Isaiah Andrews & Simon C. Essig Aberg & Jesse M. Shapiro, 2026. "A Bayesian Critic for Frequentist Procedures," NBER Working Papers 35259, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35259
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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