Quantile treatment effects and bootstrap inference under covariate-adaptive randomization

Artikel

In this paper, we study the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate-adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 11 ; Year: 2020 ; Issue: 3 ; Pages: 957-982 ; New Haven, CT: The Econometric Society

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Subject: Bootstrap inference ; quantile treatment effect

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zhang, Yichong ; Zheng, Xin
• Event: Veröffentlichung
• (who): The Econometric Society
• (where): New Haven, CT
• (when): 2020

Further information

• DOI: doi:10.3982/QE1323
• Handle: http://hdl.handle.net/10419/253588