Identification and inference in nonlinear difference-in-differences models
Identification and inference in nonlinear difference-in-differences models
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DOI：
10.1111/j.1468-0262.2006.00668.x
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发表时间：
2006-03-01
期刊：
影响因子：
6.1
通讯作者：
Imbens, GW
中科院分区：
文献类型：
--
作者：
Athey, S;Imbens, GW
本文发展了一个广泛使用的差异中的差异的方法来评估政策变化的影响。我们提出了一个模型，允许控制组和治疗组从治疗中获得不同的平均收益。所提出的模型的假设是不变的缩放的成果。我们提供的条件下，该模型是非参数识别，并提出了一个估计，可以应用于使用重复的横截面或面板数据。我们的方法提供了一个估计的整个反事实分布的结果，将已经历的治疗组在没有治疗和同样的未治疗组在治疗的存在。因此，它能够根据诸如均值-方差权衡等标准对政策干预进行评价。我们还提出了推断的方法，表明我们的估计的平均治疗效果是根N一致的，渐近正态。我们考虑扩展允许协变量，离散因变量，多组和时间段。
This paper develops a generalization of the widely used difference-in-differences method for evaluating the effects of policy changes. We propose a model that allows the control and treatment groups to have different average benefits from the treatment. The assumptions of the proposed model are invariant to the scaling of the Outcome. We provide conditions under which the model is nonparametrically identified and propose an estimator that can be applied using either repeated cross section or panel data. Our approach provides an estimate of the entire counterfactual distribution of outcomes that would have been experienced by the treatment group in the absence of the treatment and likewise for the untreated group in the presence of the treatment. Thus, it enables the evaluation of policy interventions according to criteria such as a mean-variance trade-off. We also propose methods for inference, showing that our estimator for the average treatment effect is root-N consistent and asymptotically normal. We consider extensions to allow for covariates, discrete dependent variables, and multiple groups and time periods.