Bounding Treatment Effects: A Command for the Partial Identification of the Average Treatment Effect with Endogenous and Misreported Treatment Assignment
Free accessResearch articleFirst published online June, 2015
Bounding Treatment Effects: A Command for the Partial Identification of the Average Treatment Effect with Endogenous and Misreported Treatment Assignment
We present a new command, tebounds, that implements a variety of techniques to bound the average treatment effect of a binary treatment on a binary outcome in light of endogenous and misreported treatment assignment. To tighten the worst case bounds, the monotone treatment selection, monotone treatment response, and monotone instrumental-variable assumptions of Manski and Pepper (2000, Econometrica 68: 997–1010), Kreider and Pepper (2007, Journal of the American Statistical Association 102: 432–441), Kreider et al. (2012, Journal of the American Statistical Association 107: 958–975), and Gundersen, Kreider, and Pepper (2012, Journal of Econometrics 166: 79–91) may be imposed. Imbens–Manski confidence intervals are provided.
BontempsC., MagnacT., and MaurinE.2012. Set identified linear models. Econometrica80: 1129–1155.
2.
ChernozhukovV., LeeS., and RosenA. M.2013. Intersection bounds: Estimation and inference. Econometrica81: 667–737.
3.
GundersenC., KreiderB., and PepperJ.2012. The impact of the National School Lunch Program on child health: A nonparametric bounds analysis. Journal of Econometrics166: 79–91.
4.
HiranoK., and PorterJ. R.2012. Impossibility results for nondifferentiable functionals. Econometrica80: 1769–1790.
5.
ImbensG. W., and ManskiC. F.2004. Confidence intervals for partially identified parameters. Econometrica72: 1845–1857.
6.
KolenikovS.2010. Resampling variance estimation for complex survey data. Stata Journal10: 165–199.
7.
KreiderB., and PepperJ. V.2007. Disability and employment: Reevaluating the evidence in light of reporting errors. Journal of the American Statistical Association102: 432–441.
8.
KreiderB., PepperJ. V., GundersenC., and JolliffeD.2012. Identifying the effects of SNAP (food stamps) on child health outcomes when participation is endogenous and misreported. Journal of the American Statistical Association107: 958–975.
9.
KreiderB., PepperJ. V., and RoyM.2013. Does the women, infants, and children (WIC) program improve infant health outcomes? Unpublished manuscript.
10.
LafférsL.2013. A note on bounding average treatment effects. Economics Letters120: 424–428.
11.
ManskiC. F.2013. Public Policy in an Uncertain World: Analysis and Decisions.Cambridge, MA: Harvard University Press.
12.
ManskiC. F., and PepperJ. V.2000. Monotone instrumental variables: With an application to the returns to schooling. Econometrica68: 997–1010.
13.
ManskiC. F., and PepperJ. V.2009. More on monotone instrumental variables. Econometrics Journal12: S200–S216.
14.
MillimetD. L.2011. The elephant in the corner: A cautionary tale about measurement error in treatment effects models. Advances in Econometrics: Missing-Data Methods27A: 1–39.
15.
MillimetD. L., and TchernisR.2013. Estimation of treatment effects without an exclusion restriction: With an application to the analysis of the school breakfast program. Journal of Applied Econometrics28: 982–1017.
