To Hold Out or Not to Hold Out
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 13-059.
Length: 27 pages
Date of creation: 14 Oct 2013
Date of revision:
Bayesian Analysis; Model Selection; Principal-Agent Models; Randomized Controlled Trials;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-10-25 (All new papers)
- NEP-ECM-2013-10-25 (Econometrics)
- NEP-EXP-2013-10-25 (Experimental Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer, vol. 32(1), pages 151-159, December.
- Lamont, Owen A., 2002.
"Macroeconomic forecasts and microeconomic forecasters,"
Journal of Economic Behavior & Organization,
Elsevier, vol. 48(3), pages 265-280, July.
- Owen Lamont, 1995. "Macroeconomics Forecasts and Microeconomic Forecasters," NBER Working Papers 5284, National Bureau of Economic Research, Inc.
- Hidehiko Ichimura & Christopher R. Taber, 2000.
"Direct Estimation of Policy Impacts,"
NBER Technical Working Papers
0254, National Bureau of Economic Research, Inc.
- Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dolly Guarini).
If references are entirely missing, you can add them using this form.