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To Hold Out or Not to Hold Out

Author

Listed:
  • Frank Schorfheide

    (Department of Economics, University of Pennsylvania)

  • Kenneth I. Wolpin

    (Department of Economics, University of Pennsylvania)

Abstract

A recent literature has developed that combines two prominent empirical approaches to ex ante policy evaluation: randomized controlled trials (RCT) and structural estimation. The RCT provides a “gold-standard" estimate of a particular treatment, but only of that treatment. Structural estimation provides the capability to extrapolate beyond the experimental treatment, but is based on untestable assumptions and is subject to structural data mining. Combining the approaches by holding out from the structural estimation exercise either the treatment or control sample allows for external validation of the underlying behavioral model. Although intuitively appealing, this holdout methodology is not well grounded. For instance, it is easy to show that it is suboptimal from a Bayesian perspective. Using a stylized representation of a randomized controlled trial, we provide a formal rationale for the use of a holdout sample in an environment in which data mining poses an impediment to the implementation of the ideal Bayesian analysis and a numerical illustration of the potential benefits of holdout samples.

Suggested Citation

  • Frank Schorfheide & Kenneth I. Wolpin, 2013. "To Hold Out or Not to Hold Out," PIER Working Paper Archive 13-059, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:13-059
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    References listed on IDEAS

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    Cited by:

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    8. Paul Duetting & Michal Feldman & Tomasz Ponitka & Ermis Soumalias, 2025. "The Pseudo-Dimension of Contracts," Papers 2501.14474, arXiv.org.
    9. de Bresser, Jochem, 2021. "Evaluating the Accuracy of Counterfactuals The Role of Heterogeneous Expectations in Life Cycle Models," Other publications TiSEM a7e2b4d8-fed0-4e86-926f-d, Tilburg University, School of Economics and Management.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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