We describe the new command margte, which computes marginal and average treatment effects for a model with a binary treatment and a continuous outcome given selection on unobservables and returns. Marginal treatment effects differ from average treatment effects in instances where the impact of treatment varies within a population in correlation with unobserved characteristics. Both parametric and semiparametric estimation methods can be used with margte, and we provide evidence from a Monte Carlo simulation for when each is preferable.
BjorklundA., and MoffittR.1987. The estimation of wage gains and welfare gains in self-selection models. Review of Economics and Statistics69: 42–49.
2.
BrinchC. N., MogstadM., and WiswallM.2012. Beyond LATE with a discrete instrument. Discussion Paper No. 703, Statistics Norway Research department.
3.
CarneiroP., HeckmanJ. J., and VytlacilE. J.2011. Estimating marginal returns to education. American Economic Review101: 2754–2781.
4.
DoyleJ. J.2007. Child protection and child outcomes: Measuring the effects of foster care. American Economic Review97: 1583–1610.
5.
FanJ., and GijbelsI.1996. Local Polynomial Modelling and Its Applications.New York: Chapman & Hall/CRC.
6.
FrenchE., and TaberC.2010. Identification of models of the labor market. In Handbook of Labor Economics, ed. AshenfelterO., and CardD., 537–617. Amsterdam: Elsevier.
7.
GutierrezR. G., LinhartJ. M., and PitbladoJ. S.2003. From the help desk: Local polynomial regression and Stata plugins. Stata Journal3: 412–419.
8.
HeckmanJ. J.2010. Building bridges between structural and program evaluation approaches to evaluating policy. Journal of Economic Literature48: 356–398.
9.
HeckmanJ. J., IchimuraH., SmithJ., and ToddP.1998. Characterizing selection bias using experimental data. Econometrica66: 1017–1098.
10.
HeckmanJ. J., SchmiererD., and UrzuaS.2010. Testing the correlated random coefficient model. Journal of Econometrics158: 177–203.
11.
HeckmanJ. J., UrzuaS., and VytlacilE. J.2006. Understanding instrumental variables in models with essential heterogeneity. Review of Economics and Statistics88: 389–432.
12.
HeckmanJ. J., UrzuaS., and VytlacilE. J.2006b. Web supplement to understanding instrumental variables in models with essential heterogeneity: Estimation of treatment effects under essential heterogeneity.http://jenni.uchicago.edu/underiv/documentation_2006_03_20.pdf.
13.
HeckmanJ. J., and VytlacilE. J.2001a. Local instrumental variables. In Nonlinear Statistical Modeling: Proceedings of the Thirteenth Annual International Symposium in Economic Theory and Econometrics: Essays in Honor of Takeshi Amemiya, ed. HsiaoC., MorimuneK., and PowellJ. L., 1–46. New York: Cambridge University Press.
14.
HeckmanJ. J., and VytlacilE. J.2001b. Policy-relevant treatment effects. American Economic Review91: 107–111.
15.
LokshinM., and SajaiaZ.2004. Maximum likelihood estimation of endogenous switching regression models. Stata Journal4: 282–289.
16.
MaddalaG. S.1983. Limited-Dependent and Qualitative Variables in Econometrics.Cambridge: Cambridge University Press.