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Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms. (English) Zbl 1452.62892

Summary: 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.

MSC:

62P20 Applications of statistics to economics
62G08 Nonparametric regression and quantile regression
62G20 Asymptotic properties of nonparametric inference
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