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- W1985741016 abstract "This article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The article proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its performance using Monte Carlo simulations. Even in the exogenous case, the estimator performs better in finite samples than existing censored quantile regression estimators, and performs nearly as well as maximum likelihood estimators in cases where their distributional assumptions hold. An empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells, especially at the right tail of the distribution." @default.
- W1985741016 created "2016-06-24" @default.
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- W1985741016 date "2015-10-02" @default.
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- W1985741016 title "Treatment Effects With Censoring and Endogeneity" @default.
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- W1985741016 doi "https://doi.org/10.1080/01621459.2015.1017577" @default.
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