论文标题
双向固定效果和差异差异估计量有多种处理
Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments
论文作者
论文摘要
我们研究了具有多个治疗变量的双向固定效应回归（TWFE）。在平行趋势的假设下，我们表明，每种处理的系数都可以识别该处理效果的加权总和，可能是负重负重，以及其他治疗方法的加权总和。因此，这些估计量对异质作用不强大，并且可能会受到其他治疗效应的污染。我们进一步表明，与在恒定治疗效果下发生的情况不同，从回归中省略治疗实际上可以减少估计值的偏见。我们提出了一种替代性差异估计器，对异质效应的鲁棒性，并免疫污染问题。在我们考虑的应用中，TWFE回归确定了高度非凸的效应组合，具有较大的污染权重，其系数之一与我们的异质性抗速度估计量明显不同。
We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments' effects. We further show that omitting a treatment from the regression can actually reduce the estimator's bias, unlike what would happen under constant treatment effects. We propose an alternative difference-in-differences estimator, robust to heterogeneous effects and immune to the contamination problem. In the application we consider, the TWFE regression identifies a highly non-convex combination of effects, with large contamination weights, and one of its coefficients significantly differs from our heterogeneity-robust estimator.
