Publication Type

Journal Article

Version

submittedVersion

Publication Date

5-2021

Abstract

Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra baseline covariates in addition to the strata indicators. We propose to incorporate these extra covariates via auxiliary regressions in the estimation and inference of unconditional QTEs under CARs. We establish the consistency, limiting distribution, and validity of the multiplier bootstrap of the regression-adjusted QTE estimator. 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 derive the optimal pseudo true values for the potentially misspecified parametric model that minimize the asymptotic variance of the corresponding QTE estimator. We demonstrate the finite sample performance of the new estimation and inferential methods using simulations and provide an empirical application to a well-known dataset in education.

Keywords

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

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

First Page

1

Last Page

110

ISSN

0304-4076

Publisher

Elsevier: 24 months

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Included in

Econometrics Commons

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