Conditional independence in sample selection models

Economics Letters

Volumn 54, Issue 2, 1997, Pages 103-112

  Angrist, Joshua D ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Non random samples; Propensity score; Selection bias

Indexed keywords

EID: 0031286434     PISSN: 01651765     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0165-1765(97)00022-0     Document Type: Article

References (31)

1. Ahn, H. and J.L. Powell 1993, Semi-parametric estimation of censored selection models with a nonparametric selection mechanism, Journal of Econometrics 58, 3-29.
2. Angrist, J.D., 1995, Conditioning on the probability of selection to control selection bias, NBER Technical Working Paper 181.
3. Chamberlain, G.C., 1982, The general equivalence of Granger and Sims causality, Econometrica 50, 569-581.
4. Chamberlain, G.C., 1986, Asymptotic efficiency in semi-parametric models with censoring, Journal of Econometrics 32, 198-218.
5. Choi, K., 1992, Semiparametric estimation of sample selection models using a series expansion and the propensity score, Mimeo, (Department of Economics, University of Chicago).
6. Dawid, A.P., 1979, Conditional independence in statistical theory, Journal of the Royal Statistical Society Series B 41, 1-31.
7. Dynarski, M., 1987, The scholastic aptitude test: Participation and performance, Economics of Education Review 6, 263-273.
8. Florens, J.P. and M. Mouchart, 1982, A note on noncausality, Econometrica 50, 583-591.
9. Gronau, R., 1974, Wage comparisons: A selectivity bias, Journal of Political Economy 82, 1119-1144.
10. Ham, J. and R.J. Lalonde, 1996, The effect of sample selection and initial conditions in duration models: Evidence from experimental data, Econometrica 64, 175-205.
11. Hartigan, J. and J. Tukey, 1986, Discussion 3: Comments on "Alternative methods for evaluating the impact of interventions", by J. Heckman and R. Robb, in: H. Wainer, ed., Drawing inferences from self-selected samples (Springer, New York) 57-62.
12. Heckman, J.J., 1974, Shadow prices, market wages, and labor supply, Econometrica 42, 679-693.
13. Heckman, J.J., 1976, The common structure of statistical models of truncation, sample selection, limited dependent variables and a simple estimator for such models, Annals of Economic and Social Measurement 5, 475-492.
14. Heckman, J.J., 1979, Sample selection bias as a specification error, Econometrica 47, 153-161.
15. Heckman, J.J., 1990, Varieties of selection bias, American Economic Review 80, 313-318.
16. Heckman, J.J. and R. Robb, 1985, Alternative methods for evaluating the impact of interventions, in: J. Heckman and B. Singer, eds., Longitudinal analysis of labor market data (Cambridge University Press, New York).
17. Heckman, J.J. and R. Robb, 1986, Alternative methods for solving the problem of selection bias in evaluating the impact of treatments on outcomes, in: H. Wainer, ed., Drawing inferences from self-selected samples (Springer, New York).
18. Holland, P., 1989, It's very clear: Comment on J.J. Heckman and V.J. Hotz, "Choosing among alternative methods of estimating the impact of social programs: the case of manpower training", Journal of the American Statistical Association 84, 875-877.
19. Lee, L.-F., 1982, Some approaches to the correction of selectivity bias, Review of Economic Studies 49, 355-372.
20. Little, R.J.A., 1985, A note about models for selectivity bias, Econometrica 53, 1469-1488.
21. Mroz, T.A., 1987, The sensitivity of an empirical model of married women's hours of work to economic and statistical assumptions, Econometrica 55, 765-799.
22. Newey, W.K., 1988, Two-step series estimation of sample selection models, Mimeo. (Department of Economics, Princeton University).
23. Newey, W.K, J.L. Powell, and J. Walker, 1990, Semi-parametric estimation of selection models: Some empirical results, American Economic Review 80, 324-328.
24. Olsen, R.J., 1980, A least squares correction for selectivity bias, Econometrica 48, 1815-1820.
25. Olsen, R.J., 1982, Distributional tests for selectivity bias and a more robust likelihood estimator, International Economic Review 23, 223-240.
26. Powell, B. and L.C. Steelman, 1984, Variations in state SAT performance: Meaningful or misleading?, Harvard Education Review 54, 389-412.
27. Robinson, P.M., 1988, Root-N-consistent semi-parametric regression, Econometrica 56, 931-954.
28. Rosenbaum, P.R., 1984, The consequences of adjustment for a concomitant variable that has been affected by the treatment, Journal of the Royal Statistical Society Series A 147, 656-666.
29. Rosenbaum, P.R. and D.B. Rubin, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika 70, 41-55.
30. Rosenbaum, P.R. and D.B. Rubin, 1984, Reducing bias in observational studies using subclassification on the propensity score, Journal of the American Statistical Association 79, 516-524.
31. Wainer, H., 1986, The SAT as a social indicator: A pretty bad idea, in: H. Wainer, ed., Drawing inferences from self-selected samples (Springer, New York).