Bootstrap inference on a factor model based average treatment effects estimator
提出一种新的自助法流程，用于对基于因子模型的平均处理效应估计量进行推断，克服了现有自助法的偏差，在小样本下显著优于大样本正态推断理论。
We propose a novel bootstrap procedure for conducting inference for factor model-based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.