leebounds: Lee’s treatment effect bounds for samples with nonrandom sample selection
AbstractEven if assignment of treatment is purely exogenous, estimating treatment effects may suffer from severe bias if the available sample is subject to nonrandom sample selection/attrition. Lee (Review of Economic Studies, 2009) addresses this issue by proposing an estimator for treatment effect bounds in the presence of nonrandom sample selection. In this approach, the lower and upper bound, respectively, correspond to extreme assumptions about the missing information that are consistent with the observed data. As opposed to conventional parametric approaches to correcting for sample selection bias, such as the classical heckit estimator, Lee bounds rest on very few assumptions, namely, random assignment of treatment and monotonicity. The latter means that treatment affects selection for any individual in the same direction. I introduce the new Stata command leebounds, which implements Lee’s bounds estimator in Stata. The command allows for several options, such as tightening bounds by the use of covariates, confidence intervals for the treatment effect, and statistical inference based on a weighted bootstrap. The command is applied to data gathered from a randomized trial of the effect of financial incentives on weight-loss among obese individuals.
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 Stata Users Group in its series German Stata Users' Group Meetings 2012 with number 11.
Date of creation: 04 Jun 2012
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-13 (All new papers)
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.:
- Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2012.
"Does Money Burn Fat? Evidence from a Randomized Experiment,"
IZA Discussion Papers
6888, Institute for the Study of Labor (IZA).
- Boris Augurzky & Thomas K. Bauer & Arndt R. Reichert & Christoph M. Schmidt & Harald Tauchmann, 2012. "Does Money Burn Fat? – Evidence from a Randomized Experiment," Ruhr Economic Papers 0368, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
- James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
- David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, Wiley Blackwell, vol. 76(3), pages 1071-1102, 07.
- Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum).
If references are entirely missing, you can add them using this form.