论文标题
在具有交互式固定效果的面板数据模型中，治疗效果的置信区间
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects
论文作者
论文摘要
我们考虑使用具有互动固定效果的面板模型估算的置信区间的构建。我们首先使用BAI和NG（2021）提出的基于因子的矩阵完成技术来估计治疗效果，然后使用bootstrap方法来构建每个后期治疗单元的治疗效果的置信区间。我们的置信区间构建既不需要关于错误条款的特定分布假设，也不需要大量的处理后期。我们还确定了所提出的引导程序的有效性，即这些置信区间具有渐近正确的覆盖率概率。仿真研究表明，这些置信区间具有令人满意的有限样本表现，并且使用经典数据集的经验应用产生治疗效果的效果相似幅度和可靠的置信区间。
We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects. We first use the factor-based matrix completion technique proposed by Bai and Ng (2021) to estimate the treatment effects, and then use bootstrap method to construct confidence intervals of the treatment effects for treated units at each post-treatment period. Our construction of confidence intervals requires neither specific distributional assumptions on the error terms nor large number of post-treatment periods. We also establish the validity of the proposed bootstrap procedure that these confidence intervals have asymptotically correct coverage probabilities. Simulation studies show that these confidence intervals have satisfactory finite sample performances, and empirical applications using classical datasets yield treatment effect estimates of similar magnitudes and reliable confidence intervals.
