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首页> 外文期刊>Journal of Econometrics >Identifying the average treatment effect in ordered treatment models without unconfoundedness
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Identifying the average treatment effect in ordered treatment models without unconfoundedness

机译:在有序的治疗模型中确定平均治疗效果

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摘要

We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show nonparametric point identification and estimation of the ATE. We apply our model to reinvestigate the inverted-U relationship between competition and innovation, and find no inverted-U in US data. (C) 2016 Elsevier B.V. All rights reserved.
机译:当通过横截面或面板模型中的有序选择指定治疗时,我们将显示平均治疗效果(ATE)的标识。通过相对于两个或多个阈值的潜在变量(包含连续仪器)的位置确定治疗。我们没有对潜在错误和潜在结果进行功能形式限制。不存在混淆的治疗方法,无法确定治疗的无穷大。但是,我们仍然显示了ATE的非参数点识别和估计。我们应用我们的模型对竞争与创新之间的倒U关系进行了重新调查,在美国数据中未发现倒U。 (C)2016 Elsevier B.V.保留所有权利。

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