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ZHAN Mingfeng1, FANG Ying1,2, LIN Ming1,2
[1] Rubin D B, Inference and missing data, Biometrika, 1976, 63(3): 581–592. [2] Imbens G W and Wooldridge J M, Recent developments in the econometrics of program evaluation, Journal of Economic Literature, 2009, 47(1): 5–86. [3] Liu Z, Cai Z, Fang Y, et al., Statistical analysis and evaluation of macroeconomic policies: A selective review, Applied Mathematics — A Journal of Chinese Universities, 2020, 35(1): 57–83. [4] Tang S, Some recent developments in modeling quantile treatment effects, Applied Mathematics — A Journal of Chinese Universities, 2020, 35(2): 220–243. [5] Rosenbaum P R and Rubin D B, The central role of the propensity score in observational studies for causal effects, Biometrika, 1983, 70(1): 41–55. [6] Kang J D and Schafer J L, Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data, Statistical Science, 2007, 22(4): 523–539. [7] Imai K and Ratkovic M, Covariate balancing propensity score, Journal of the Royal Statistical Society: Series B, 2014, 76(1): 243–263. [8] Hansen L P, Large sample properties of generalized method of moments estimators, Econometrica, 1982, 50(4): 1029–1054. [9] Zubizarreta J R, Stable weights that balance covariates for estimation with incomplete outcome data, Journal of the American Statistical Association, 2015, 110(511): 910–922. [10] Chan K C G, Yam S C P, and Zhang Z, Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting, Journal of the Royal Statistical Society: Series B, 2016, 78(3): 673–700. [11] Kitamura Y and Stutzer M, An information-theoretic alternative to generalized method of moments estimation, Econometrica, 1997, 65(4): 861–874. [12] Owen A B, Empirical likelihood ratio confidence intervals for a single functional, Biometrika, 1988, 75(2): 237–249. [13] Hansen L P, Heaton J, and Yaron A, Finite sample properties of some alternative GMM estimators, Journal of Business & Economic Statistics, 1996, 14(3): 262–280. [14] Sant’Anna P H, Song X, and Xu Q, Covariate distribution balance via propensity scores, arXiv preprint arXiv: 1810.01370v4, 2020. [15] Hainmueller J, Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies, Political Analysis, 2012, 20(1): 25–46. [16] Zhao Q, Covariate balancing propensity score by tailored loss functions, Annals of Statistics, 2019, 47(2): 965–993. [17] Wang Y and Zubizarreta J R, Minimal dispersion approximately balancing weights: Asymptotic properties and practical considerations, Biometrika, 2020, 107(1): 93–105. [18] Wan S K, Xie Y, and Hsiao C, Panel data approach vs synthetic control method, Economics Letters, 2018, 164(3): 121–123. [19] González-Manteiga W and Crujeiras R M, An updated review of goodness-of-fit tests for regression models, Test, 2013, 22(3): 361–411. [20] Donald S G and Hsu Y C, Estimation and inference for distribution functions and quantile functions in treatment effect models, Journal of Econometrics, 2014, 178(3): 383–397. [21] Firpo S, Efficient semiparametric estimation of quantile treatment effects, Econometrica, 2007, 75(1): 259–276. [22] Melly B, Estimation of counterfactual distributions using quantile regression, Discussion Paper, Universität St. Gallen, 2006, http://www.alexandria.unisg.ch/Publikationen/22644. [23] Hahn J, On the role of the propensity score in efficient semiparametric estimation of average treatment effects, Econometrica, 1998, 66(2): 315–331. [24] Hirano K, Imbens G W, and Ridder G, Efficient estimation of average treatment effects using the estimated propensity score, Econometrica, 2003, 71(4): 1161–1189. [25] Advani A, Kitagawa T, and Słoczyński T, Mostly harmless simulations? Using Monte Carlo studies for estimator selection, Journal of Applied Econometrics, 2019, 34(6): 893–910. [26] Chen T, Ji Y, Zhou Y, et al., Testing conditional mean independence under symmetry, Journal of Business & Economic Statistics, 2018, 36(4): 615–627. |
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