Liang Jiang, Peter C. B. Phillips, Yubo Tao, Yichong Zhang
arXiv 31 May 2021 · Econometrics · publishedJournal of Econometrics (2022) · 13 citations (OpenAlex)
arXiv:2105.14752 · PDF · DOI · OpenAlex · Extracted main text
Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these additional covariates via auxiliary regressions in the estimation and inference of unconditional quantile treatment effects (QTEs) under CARs. We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability in the limit under the null. When the auxiliary regression is correctly specified, the regression-adjusted estimator achieves the minimum asymptotic variance. We also discuss forms of adjustments that can improve the efficiency of the QTE estimators. The finite sample performance of the new estimation and inferential methods is studied in simulations and an empirical application to a well-known dataset concerned with expanding access to basic bank accounts on savings is reported.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2018) Inference under covariate-adaptive randomization | 0.888 | 10 | 5 | 70% |
| 2 | Zhang, Y. and X. Zheng (2020) Quantile treatment effects and bootstrap inference under covariate-adaptive randomization self | 0.865 | 17 | 9 | 65% |
| 3 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2019) Inference under covariate-adaptive randomization with multiple treatments | 0.737 | 3 | 2 | 100% |
| 4 | Liu, H., F. Tu, and W. Ma (2020) A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond | 0.737 | 3 | 2 | 100% |
| 5 | Dupas, P., D. Karlan, J. Robinson, and D. Ubfal (2018) Banking the unbanked? evidence from three countries | 0.644 | 4 | 1 | 100% |
| 6 | Hirano, K., G. W. Imbens, and G. Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.644 | 3 | 2 | 67% |
| 7 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program evaluation with high-dimensional data | 0.630 | 8 | 6 | 25% |
| 8 | van der Vaart, A. and J. A. Wellner (1996) Weak Convergence and Empirical Processes | 0.585 | 3 | 3 | 33% |
| 9 | Shao, J., X. Yu, and B. Zhong (2010) A theory for testing hypotheses under covariate-adaptive randomization | 0.585 | 3 | 1 | 100% |
| 10 | Bai, Y (2020) Optimality of matched-pair designs in randomized controlled trials | 0.511 | 2 | 1 | 100% |
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