论文标题
没有单调性的弱模型中的内生性
Endogeneity in Weakly Separable Models without Monotonicity
论文作者
论文摘要
当潜在的二元性内源治疗弱可分离时，我们确定并估计治疗效果。 Vytlacil和Yildiz（2007）提出了一种识别策略，以利用观察到的结果的平均值，但它们的方法需要单调性条件。相比之下，我们利用整个结果分布中的完整信息，而不仅仅是其均值。结果，我们的方法不需要单调性，也适用于具有多个指数的一般设置。我们提供的示例可以在其中我们的方法可以识别感兴趣的治疗效应参数，而现有方法将失败。其中包括潜在结果取决于多个未观察到的干扰项的模型，例如ROY模型，多项式选择模型以及具有内源随机系数的模型。我们建立了估计量的一致性和渐近正态性。
We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general settings with multiple indices. We provide examples where our approach can identify treatment effect parameters of interest whereas existing methods would fail. These include models where potential outcomes depend on multiple unobserved disturbance terms, such as a Roy model, a multinomial choice model, as well as a model with endogenous random coefficients. We establish consistency and asymptotic normality of our estimators.
