Efficient and Robust Estimation of the Generalized LATE Model

References

• Abadie, A. (2003), “Semiparametric Instrumental Variable Estimation of Treatment Response Models,” Journal of Econometrics, 113, 231–263. DOI: 10.1016/S0304-4076(02)00201-4.
   Web of Science ®Google Scholar
• Ackerberg, D., Chen, X., Hahn, J., and Liao, Z. (2014), “Asymptotic Efficiency of Semiparametric Two-Step GMM,” Review of Economic Studies, 81, 919–943. DOI: 10.1093/restud/rdu011.
   Web of Science ®Google Scholar
• Aliprantis, D., and Richter, F. G.-C. (2020), “Evidence of Neighborhood Effects from Moving to Opportunity: Lates of Neighborhood Quality,” The Review of Economics and Statistics, 102, 633–647. DOI: 10.1162/rest_a_00933.
   Web of Science ®Google Scholar
• Andrews, D. W., Cheng, X., and Guggenberger, P. (2020), “Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests,” Journal of Econometrics, 218, 496–531. DOI: 10.1016/j.jeconom.2020.04.027.
   Web of Science ®Google Scholar
• Andrews, I., and Armstrong, T. B. (2017), “Unbiased Instrumental Variables Estimation Under Known First-Stage Sign,” Quantitative Economics, 8, 479–503. DOI: 10.3982/QE700.
   Web of Science ®Google Scholar
• Angrist, J. D., Graddy, K., and Imbens, G. W. (2000), “The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish,” The Review of Economic Studies, 67, 499–527. DOI: 10.1111/1467-937X.00141.
   Web of Science ®Google Scholar
• Angrist, J. D., and Imbens, G. W. (1995), “Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity,” Journal of the American statistical Association, 90, 431–442. DOI: 10.1080/01621459.1995.10476535.
   Web of Science ®Google Scholar
• Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–455. DOI: 10.1080/01621459.1996.10476902.
   Web of Science ®Google Scholar
• Ao, W., Calonico, S., and Lee, Y.-Y. (2021), “Multivalued Treatments and Decomposition Analysis: An Application to the WIA Program,” Journal of Business & Economic Statistics, 39, 358–371. DOI: 10.1080/07350015.2019.1660664.
   Web of Science ®Google Scholar
• Bickel, P. J., Klaassen, C. A., Ritov, Y., and Wellner, J. A. (1993), Efficient and Adaptive Estimation for Semiparametric Models (Vol. 4), New York: Springer.
   Google Scholar
• Cattaneo, M. D. (2010), “Efficient Semiparametric Estimation of Multi-Valued Treatment Effects Under Ignorability,” Journal of Econometrics, 155, 138–154. DOI: 10.1016/j.jeconom.2009.09.023.
   Web of Science ®Google Scholar
• Chen, X., and Christensen, T. M. (2015), “Optimal Uniform Convergence Rates and Asymptotic Normality for Series Estimators Under Weak Dependence and Weak Conditions,” Journal of Econometrics, 188, 447–465. Heterogeneity in Panel Data and in Nonparametric Analysis in honor of Professor Cheng Hsiao. DOI: 10.1016/j.jeconom.2015.03.010.
   Web of Science ®Google Scholar
• Chen, X., Hong, H., and Tarozzi, A. (2008), “Semiparametric Efficiency in GMM Models with Auxiliary Data,” The Annals of Statistics, 36, 808–843. DOI: 10.1214/009053607000000947.
   Web of Science ®Google Scholar
• Chen, X., Linton, O., and Van Keilegom, I. (2003), “Estimation of Semiparametric Models When the Criterion Function is not Smooth,” Econometrica, 71, 1591–1608. DOI: 10.1111/1468-0262.00461.
   Web of Science ®Google Scholar
• Chen, X., and Santos, A. (2018), “Overidentification in Regular Models,” Econometrica, 86, 1771–1817. DOI: 10.3982/ECTA13559.
   Web of Science ®Google Scholar
• Chen, Y.-C., and Xie, H. (2022), “Global Representation of the Conditional Late Model: A Separability Result,” Oxford Bulletin of Economics and Statistics, 84, 789–798. DOI: 10.1111/obes.12476.
   Web of Science ®Google Scholar
• Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. (2018), “Double/Debiased Machine Learning for Treatment and Structural Parameters,” The Econometrics Journal, 21, C1–C68. DOI: 10.1111/ectj.12097.
   Web of Science ®Google Scholar
• Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and Robins, J. M. (2022), “Locally Robust Semiparametric Estimation,” Econometrica, 90, 1501–1535. DOI: 10.3982/ECTA16294.
   Web of Science ®Google Scholar
• Farrell, M. H. (2015), “Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations,” Journal of Econometrics, 189, 1–23. DOI: 10.1016/j.jeconom.2015.06.017.
   Web of Science ®Google Scholar
• Finkelstein, A. (2013), “Oregon Health Insurance Experiment Public Use Data,” available at http://www.nber.org/oregon/data.html.
   Google Scholar
• Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J., Newhouse, J. P., Allen, H., Baicker, K., and Group, O. H. S. (2012), “The Oregon Health Insurance Experiment: Evidence from the First Year,” The Quarterly Journal of Economics, 127, 1057–1106. DOI: 10.1093/qje/qjs020.
   PubMed Web of Science ®Google Scholar
• Flores, C. A., Flores-Lagunes, A., Gonzalez, A., and Neumann, T. C. (2012), “Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps,” The Review of Economics and Statistics, 94, 153–171. DOI: 10.1162/REST_a_00177.
   Web of Science ®Google Scholar
• Friedman, J., Tibshirani, R., and Hastie, T. (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
   PubMed Web of Science ®Google Scholar
• Frölich, M. (2007), “Nonparametric iv Estimation of Local Average Treatment Effects with Covariates,” Journal of Econometrics, 139, 35–75. DOI: 10.1016/j.jeconom.2006.06.004.
   Web of Science ®Google Scholar
• Galindo, C. (2021), “Essays on Treatment Effects from Multiple Unordered Choices,” Ph. D. thesis, University of Maryland, College Park.
   Google Scholar
• Goff, L. (2020), “A Vector Monotonicity Assumption for Multiple Instruments,” arXiv preprint arXiv:2009.00553.
   Google Scholar
• Hahn, J. (1998), “On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects,” Econometrica, 66, 315–331. DOI: 10.2307/2998560.
   Web of Science ®Google Scholar
• Hansen, B. E. (2008), “Uniform Convergence Rates for Kernel Estimation with Dependent Data,” Econometric Theory, 24, 726–748. DOI: 10.1017/S0266466608080304.
   Web of Science ®Google Scholar
• Heckman, J. J., Ichimura, H., and Todd, P. (1998), “Matching as an Econometric Evaluation Estimator,” The Review of Economic Studies, 65, 261–294. DOI: 10.1111/1467-937X.00044.
   Web of Science ®Google Scholar
• Heckman, J. J., and Pinto, R. (2018), “Unordered Monotonicity,” Econometrica, 86, 1–35. DOI: 10.3982/ECTA13777.
   PubMed Web of Science ®Google Scholar
• Heckman, J. J., and Vytlacil, E. (2005), “Structural Equations, Treatment Effects, and Econometric Policy Evaluation,” Econometrica, 73, 669–738. DOI: 10.1111/j.1468-0262.2005.00594.x.
   Web of Science ®Google Scholar
• Hirano, K., Imbens, G. W., and Ridder, G. (2003), “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” Econometrica, 71, 1161–1189. DOI: 10.1111/1468-0262.00442.
   Web of Science ®Google Scholar
• Hong, H., and Nekipelov, D. (2010), “Semiparametric Efficiency in Nonlinear Late Models,” Quantitative Economics, 1, 279–304. DOI: 10.3982/QE43.
   Web of Science ®Google Scholar
• Huber, M., Hsu, Y.-C., Lee, Y.-Y., and Lettry, L. (2020), “Direct and Indirect Effects of Continuous Treatments based on Generalized Propensity Score Weighting,” Journal of Applied Econometrics, 35, 814–840. DOI: 10.1002/jae.2765.
   Web of Science ®Google Scholar
• Ichimura, H., and Newey, W. K. (2022), “The Influence Function of Semiparametric Estimators,” Quantitative Economics, 13, 29–61. DOI: 10.3982/QE826.
   Web of Science ®Google Scholar
• Imbens, G. W., and Angrist, J. D. (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475. DOI: 10.2307/2951620.
   Web of Science ®Google Scholar
• Imbens, G. W., and Manski, C. F. (2004), “Confidence Intervals for Partially Identified Parameters,” Econometrica, 72, 1845–1857. DOI: 10.1111/j.1468-0262.2004.00555.x.
   Web of Science ®Google Scholar
• Kitagawa, T. (2015), “A Test for Instrument Validity,” Econometrica, 83, 2043–2063. DOI: 10.3982/ECTA11974.
   Web of Science ®Google Scholar
• Kline, P., and Walters, C. R. (2016), “Evaluating Public Programs with Close Substitutes: The Case of Head Start,” The Quarterly Journal of Economics, 131, 1795–1848. DOI: 10.1093/qje/qjw027.
   Web of Science ®Google Scholar
• Lee, S., and Salanié, B. (2018), “Identifying Effects of Multivalued Treatments,” Econometrica, 86, 1939–1963. DOI: 10.3982/ECTA14269.
   Web of Science ®Google Scholar
• Lee, Y.-Y. (2018), “Efficient Propensity Score Regression Estimators of Multivalued Treatment Effects for the Treated,” Journal of Econometrics, 204, 207–222. DOI: 10.1016/j.jeconom.2018.02.002.
   Web of Science ®Google Scholar
• Masry, E. (1996), “Multivariate Local Polynomial Regression for Time Series: Uniform Strong Consistency and Rates,” Journal of Time Series Analysis, 17, 571–599. DOI: 10.1111/j.1467-9892.1996.tb00294.x.
   Google Scholar
• Mikusheva, A. (2007), “Uniform Inference in Autoregressive Models,” Econometrica, 75, 1411–1452. DOI: 10.1111/j.1468-0262.2007.00798.x.
   Web of Science ®Google Scholar
• Mogstad, M., Torgovitsky, A., and Walters, C. R. (2021), “The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables,” American Economic Review, 111, 3663–98. DOI: 10.1257/aer.20190221.
   Web of Science ®Google Scholar
• Mogstad, M., Torgovitsky, A., and Walters, C. R. (2023), “Policy Evaluation with Multiple Instrumental Variables,” Journal of Econometrics, forthcoming.
   Google Scholar
• Mountjoy, J. (2022), “Community Colleges and Upward Mobility,” American Economic Review, 112, 2580–2630. DOI: 10.1257/aer.20181756.
   Web of Science ®Google Scholar
• Navjeevan, M., Pinto, R., and Santos, A. (2023), “Identification and Estimation in a Class of Potential Outcomes Models,” arXiv preprint arXiv:2310.05311.
   Google Scholar
• Newey, W. K. (1990), “Semiparametric Efficiency Bounds,” Journal of Applied Econometrics, 5, 99–135. DOI: 10.1002/jae.3950050202.
   Web of Science ®Google Scholar
• ——- (1994), “The Asymptotic Variance of Semiparametric Estimators,” Econometrica, 62, 1349–1382.
   Web of Science ®Google Scholar
• Newey, W. K., and Stouli, S. (2021), “Heterogeneous Coefficients, Control Variables and Identification of Multiple Treatment Effects,” Biometrika, 109, 865–872. DOI: 10.1093/biomet/asab060.
   Web of Science ®Google Scholar
• Okui, R., Small, D. S., Tan, Z., and Robins, J. M. (2012), “Doubly Robust Instrumental Variable Regression,” Statistica Sinica, 22, 173–205. DOI: 10.5705/ss.2009.265.
   Web of Science ®Google Scholar
• Pinto, R. (2021), “Beyond Intention to Treat: Using the Incentives in Moving to Opportunity to Identify Neighborhood Effects,” UCLA working paper.
   Google Scholar
• Simon, N., Friedman, J., Tibshirani, R., and Hastie, T. (2011), “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent,” Journal of Statistical Software, 39, 1–13. DOI: 10.18637/jss.v039.i05.
   PubMed Web of Science ®Google Scholar
• Singh, R., and Sun, L. (2022), “Double Robustness for Complier Parameters and A Semiparametric Test for Complier Characteristics,” arXiv preprint arXiv:1909.05244.
   Google Scholar
• Słoczyński, T., and Wooldridge, J. M. (2018), “A General Double Robustness Result for Estimating Average Treatment Effects,” Econometric Theory, 34, 112–133. DOI: 10.1017/S0266466617000056.
   Web of Science ®Google Scholar
• Stone, C. J. (1982), “Optimal Global Rates of Convergence for Nonparametric Regression,” The Annals of Statistics, 10, 1040–1053. DOI: 10.1214/aos/1176345969.
   Web of Science ®Google Scholar
• Sun, Z. (2023), “Instrument Validity for Heterogeneous Causal Effects,” Journal of Econometrics accepted. DOI: 10.1016/j.jeconom.2023.105523.
   Web of Science ®Google Scholar
• Tan, Z. (2006), “Regression and Weighting Methods for Causal Inference Using Instrumental Variables,” Journal of the American Statistical Association, 101, 1607–1618. DOI: 10.1198/016214505000001366.
   Web of Science ®Google Scholar
• Tay, J. K., Narasimhan, B., and Hastie, T. (2023), “Elastic Net Regularization Paths for All Generalized Linear Models,” Journal of Statistical Software, 106, 1–31. DOI: 10.18637/jss.v106.i01.
   PubMed Web of Science ®Google Scholar
• Uysal, S. D. (2015), “Doubly Robust Estimation of Causal Effects with Multivalued Treatments: An Application to the Returns to Schooling,” Journal of Applied Econometrics, 30, 763–786. DOI: 10.1002/jae.2386.
   Web of Science ®Google Scholar
• van der Vaart, A. W. (1998). Asymptotic Statistics (Vol. 3), Cambridge: Cambridge University Press.
   Google Scholar
• Vazquez-Bare, G. (2022), “Causal Spillover Effects Using Instrumental Variables,” Journal of the American Statistical Association, 118, 1911–1922. DOI: 10.1080/01621459.2021.2021920.
   Web of Science ®Google Scholar
• Vytlacil, E. (2002), “Independence, Monotonicity, and Latent Index Models: An Equivalence Result,” Econometrica, 70, 331–341. DOI: 10.1111/1468-0262.00277.
   Web of Science ®Google Scholar