Estimation of treatment effects under endogenous heteroskedasticity

Jason Abrevaya and Haiqing Xu

Journal of Econometrics, 2023, vol. 234, issue 2, 451-478

Abstract: This paper considers a treatment effect model in which individual treatment effect may be heterogeneous, even among observationally identical individuals. Specifically, by extending the classical instrumental-variables (IV) model with an endogenous binary treatment, the heteroskedasticity of the error disturbance is allowed to vary with the treatment variable so that the treatment generates both mean and variance effect on the outcome. In this endogenous heteroskedasticity IV (EHIV) model, the standard IV estimator can be inconsistent for the average treatment effect (ATE) and lead to incorrect inference. After nonparametric identification is established, closed-form estimators are provided under the linear EHIV specification for the mean and variance treatment effect, as well as the average treatment effect on the treated (ATT). Asymptotic properties of the estimators are derived. We use Monte Carlo experiments to investigate the performance of the proposed approach and then consider an empirical application regarding the effect of fertility on female labor supply. Our findings demonstrate the importance of accounting for endogenous heteroskedasticity.

Keywords: Endogenous heteroskedasticity; Individual treatment effect; Average treatment effect; Local average treatment effect; Instrumental variable (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:234:y:2023:i:2:p:451-478

DOI: 10.1016/j.jeconom.2021.01.011

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