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To Hold Out or Not to Hold Out

  • Frank Schorfheide
  • Kenneth I. Wolpin

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 19565.

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Date of creation: Oct 2013
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Handle: RePEc:nbr:nberwo:19565
Note: DEV ED EFG HE LS ME PE
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  1. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
  2. Hidehiko Ichimura & Christopher Taber, 2000. "Direct estimation of policy impacts," IFS Working Papers W00/05, Institute for Fiscal Studies.
  3. Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer, vol. 32(1), pages 151-159, December.
  4. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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