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To Hold Out or Not to Hold Out


  • Frank Schorfheide

    () (Department of Economics, University of Pennsylvania)

  • Kenneth I. Wolpin

    () (Department of Economics, University of Pennsylvania)


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

    1. Esther Duflo & Rema Hanna & Stephen P. Ryan, 2012. "Incentives Work: Getting Teachers to Come to School," American Economic Review, American Economic Association, vol. 102(4), pages 1241-1278, June.
    2. Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer;Game Theory Society, vol. 32(1), pages 151-159, December.
    3. Christopher Ferrall, 2012. "Explaining and Forecasting Results of the Self-sufficiency Project," Review of Economic Studies, Oxford University Press, vol. 79(4), pages 1495-1526.
    4. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    5. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    6. Wolpin, Kenneth I., 2013. "The Limits of Inference without Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262019086, January.
    7. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 293-318.
    8. Hidehiko Ichimura & Christopher R. Taber, 2000. "Direct Estimation of Policy Impacts," NBER Technical Working Papers 0254, National Bureau of Economic Research, Inc.
    9. Petra E. Todd & Kenneth I. Wolpin, 2008. "Ex Ante Evaluation of Social Programs," Annals of Economics and Statistics, GENES, issue 91-92, pages 263-291.
    10. repec:adr:anecst:y:2008:i:91-92:p:13 is not listed on IDEAS
    11. Frank Schorfheide & Kenneth I. Wolpin, 2012. "On the Use of Holdout Samples for Model Selection," American Economic Review, American Economic Association, vol. 102(3), pages 477-481, May.
    12. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    13. repec:adr:anecst:y:2008:i:91-92 is not listed on IDEAS
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    Cited by:

    1. John Rust, 2014. "The Limits of Inference with Theory: A Review of Wolpin (2013)," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 820-850, September.

    More about this item


    Bayesian Analysis; Model Selection; Principal-Agent Models; Randomized Controlled Trials;

    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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