Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2023

Abstract

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

Keywords

Covariate-adaptive randomization, high-dimensional data, regression adjustment, quantile treatment effects

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

234

Issue

2

First Page

758

Last Page

776

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2022.08.010

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.jeconom.2022.08.010

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