Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms

Heng Chen and Yanqin Fan

Journal of Econometrics, 2019, vol. 212, issue 2, 476-502

Abstract: This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the agent’s selection of treatment is affected by either a discontinuous or kink incentive assignment mechanism and some unobservable characteristic. For each assignment mechanism, we (i) establish identification and propose a local wavelet estimator of the W-ATE; (ii) establish asymptotic properties of the local wavelet estimator including optimal convergence rate and asymptotic normality; and (iii) investigate the finite sample performance of the local wavelet estimators and compare them with local polynomial estimators via an extensive simulation study. We also propose an identification-robust wavelet estimator of the W-ATE.

Keywords: Regression discontinuity design; Regression kink design; Finite-sample variance; Wavelet transformation (search for similar items in EconPapers)
JEL-codes: C13 C14 C35 C51 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:2:p:476-502

DOI: 10.1016/j.jeconom.2019.05.015

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