论文标题
互动固定效应模型的治疗效果少数时间段
Treatment Effects in Interactive Fixed Effects Models with a Small Number of Time Periods
论文作者
论文摘要
本文考虑并识别当未经处理的潜在结果由交互式固定效应模型产生时，识别和估计对治疗（ATT）的平均治疗效应。也就是说，除了时间周期和个体固定效应外，我们考虑了存在一个未观察到的时间不变变量的情况，该变量对未经处理的潜在结果的影响可能会随着时间而变化，因此可能会导致与未经培养的组相对于处理过的组的不同路径（在没有治疗的情况下）遵循不同的路径。我们在本文中考虑的模型概括了治疗效果文献中许多常用模型，包括差异和特定特定线性趋势模型的差异。与大多数有关交互式固定效应模型的文献不同，我们不需要到Infinity的时间段来始终如一地估计ATT。我们的主要识别结果依赖于具有一段时间不变的协变量（例如种族或性别）的影响不会随着时间而变化。使用我们的方法，我们表明可以将ATT与三个时期以及面板或重复的横截面数据一起识别。
This paper considers identifying and estimating the Average Treatment Effect on the Treated (ATT) when untreated potential outcomes are generated by an interactive fixed effects model. That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential outcomes may change over time and which can therefore cause outcomes (in the absence of participating in the treatment) to follow different paths for the treated group relative to the untreated group. The models that we consider in this paper generalize many commonly used models in the treatment effects literature including difference in differences and individual-specific linear trend models. Unlike the majority of the literature on interactive fixed effects models, we do not require the number of time periods to go to infinity to consistently estimate the ATT. Our main identification result relies on having the effect of some time invariant covariate (e.g., race or sex) not vary over time. Using our approach, we show that the ATT can be identified with as few as three time periods and with panel or repeated cross sections data.
