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To Hold Out or Not to Hold Out

Author

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  • Schorfheide, Frank

    (University of PA)

  • Wolpin, Kenneth I.

    (Rice University and University of PA)

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 the 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

  • Schorfheide, Frank & Wolpin, Kenneth I., 2014. "To Hold Out or Not to Hold Out," Working Papers 14-018, Rice University, Department of Economics.
    • Handle: RePEc:ecl:riceco:14-018
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    File URL: http://economics.rice.edu/rise/working-papers/hold-out-or-not-hold-out
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    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," The Review of Economic Studies, Review of Economic Studies Ltd, 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. Kenneth I. Wolpin & Petra E. Todd, 2006. "Assessing the Impact of a School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility," American Economic Review, American Economic Association, vol. 96(5), pages 1384-1417, December.
    6. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    7. Wolpin, Kenneth I., 2013. "The Limits of Inference without Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262019086, December.
    8. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 293-318.
    9. Hidehiko Ichimura & Christopher R. Taber, 2000. "Direct Estimation of Policy Impacts," NBER Technical Working Papers 0254, National Bureau of Economic Research, Inc.
    10. 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.
    11. repec:adr:anecst:y:2008:i:91-92:p:13 is not listed on IDEAS
    12. 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.
    13. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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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
    • 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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