References
- Ahn, H., and Powell, J. L. (1993), “Semiparametric Estimation of Censored Selection Models With a Nonparametric Selection Mechanism,” Journal of Econometrics, 58, 3–29.
- Andrews, D. W. K., and Schafgans, M. (1998), “Semiparametric Estimation of the Intercept of a Sample Selection Model,” Review of Economic Studies, 65, 497–517.
- Angrist, J. D. (2001), “Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors: Simple Strategies for Empirical Practice,” Journal of Business & Economic Statistics, 19, 2–16.
- Angrist, J. D., Imbens, G., and Rubin, D. (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–455.
- Bertanha, M. (2016), “Regression Discontinuity Design With Many Thresholds,” Working Paper.
- Blanco, G., Flores, C. A., and Flores-Lagunes, A. (2013), “The Effects of Job Corps Training on Wages of Adolescents and Young Adults,” American Economic Review: P&P, 103, 418–422.
- Calonico, S., Cattaneo, M. D., and Titiunik, R. (2014), “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs,” Econometrica, 82, 2295–2326.
- Cattaneo, M. D., Jansson, M., and Ma, X. (2016), “Simple Local Regression Distribution Estimators With an Application to Manipulation Testing,” Working paper.
- Chen, X., and Flores, C. A. (2014), “Bounds on Treatment Effects in the Presence of Sample Selection and Noncompliance: The Wage Effects of Job Corps,” Working Paper.
- Das, M., Newey, W. K., and Vella, F. (2003), “Nonparametric Estimation of Sample Selection Models,” Review of Economic Studies, 70, 33–58.
- De Chaisemartin, C. (2014), “Tolerating Defiance? Local Average Treatment Effects without Monotonicity,” working paper.
- Dong, Y. (2016), “An Alternative Assumption to Identify LATE in Regression Discontinuity Models,” working paper.
- Dong, Y., and Shen, S. (2016), “Testing for Rank Invariance or Similarity in Program Evaluation,” working paper.
- Fletcher, J. M., and Tokmouline, M. (2010), “The Effects of Academic Probation on College Success: Lending Students a Hand or Kicking Them While They are Down,” working paper.
- Frandsen, R. B. (2015), “Treatment Effects With Censoring and Endogeneity,” Journal of the American Statistical Association, 110, 1745–1752.
- Frandsen, B. R., Frölich, M., and Melly, B. (2012), “Quantile Treatment Effects in the Regression Discontinuity Design,” Journal of Econometrics, 168, 382–395.
- Frangakis, C. E., and Rubin, D. B. (2002), “Principal Stratification and Causal Inference,” Biometrics, 58, 21–29.
- Hahn, J., Todd, P., and van der Klaauw, W. (2001), “Identification and Estimation of Treatment Effects With a Regression-Discontinuity Design,” Econometrica, 69, 201–209.
- Heckman, J. J. (1979), “Sample Selection Bias as a Specification Error,” Econometrica, 47, 153–161.
- ——— (1990), “Varieties of Selection Bias,” American Economic Review, P&P, 80, 313–318.
- Heckman, J. J., Smith, J., and Clements, N. (1997), “Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts,” Review of Economic Studies, 64, 487–535.
- Horowitz, J. L., and Manski, C. F. (1995), “Identification and Robustness With Contaminated and Corrupted Data,” Econometrica, 63, 281–302.
- ——— (2000), “Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data,” Journal of the American Statistical Association, 95, 77–84.
- Imai, K. (2008), “Sharp Bounds on the Causal Effects in Randomized Experiments With” Truncation-by-Death,” Statistics and Probability Letters, 78, 144–149.
- Imbens, G. W., and Kalyanaraman, K. (2012), “Optimal Bandwidth Choice for the Regression Discontinuity Estimator,” Review of Economic Studies, 79, 933–959.
- Imbens, G. W., and Manski, C. F. (2004), “Confidence Intervals for Partially Identified Parameters,” Econometrica, 72, 1845–1857.
- Keele, L. J., and Titiunik, R. (2015), “Geographic Boundaries as Regression Discontinuities,” Political Analysis, 23, 127–155.
- Kim, B. M. (2012), “Do Developmental Mathematics Courses Develop the Mathematics?” working paper.
- Kitigawa, T. (2015), “A Test for Instrument Validity,” Econometrica, 83, 2043–2063.
- Lee, D. S. (2008), “Randomized Experiments From Non-Random Selection in U.S. House Elections,” Journal of Econometrics, 142, 675–697.
- ——— (2009), “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects,” Review of Economic Studies, 76, 1071–1102.
- Lewbel, A. (2007), “Endogenous Selection or Treatment Model Estimation,” Journal of Econometrics, 141, 777–806.
- Lindo, M. J., Sanders, N. J., and Oreopoulos, P. (2010), “Ability, Gender, and Performance Standards: Evidence From Academic Probation,” American Economic Journal: Applied Economics, 2, 95–117.
- McCrary, J. (2008), “Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test,” Journal of Econometrics, 142, 698–714.
- McCrary, J., and Royer, H. (2011), “The Effect of Female Education on Fertility and Infant Health: Evidence from School Entry Policies Using Exact Date of Birth,” American Economic Review, 101, 158–195.
- Martorell, P., and McFarlin, I., Jr. (2011), “Help or Hindrance? The Effects of College Remediation on Academic and Labor Market Outcomes,” Review of Economics and Statistics, 93, 436–454.
- Staub, E. K. (2014), “A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models,” Review of Economics and Statistics, 96, 371–375.
- Stoye, J. (2009), “More on Confidence Intervals for Partially Identified Parameters,” Econometrica, 77, 1299–1315.
- Vytlacil, E. (2002), “Independence, Monotonicity, and Latent Index Models: An Equivalence Result,” Econometrica, 70, 331–341.
- Zhang, J. L., and Rubin, D. B. (2003), “Estimation of Causal Effects via Principle Stratification When Some Outcomes are Truncated by “Death”,” Journal of Educational Behavioral Statistics, 28, 353–368.