论文标题
具有群集依赖性的高维线性模型的强大推断
Robust Inference in High Dimensional Linear Model with Cluster Dependence
论文作者
论文摘要
群集标准误差（Liang and Zeger，1986）被经验研究人员广泛使用，以说明线性模型中的群集依赖性。众所周知，此标准误差有偏差。我们表明，通过重新审视Chesher and Jewitt（1987）的方法，在高维渐近学下的偏见不会消失。在Cattaneo，Jansson和Newey（2018）引入的高维设置下，提供了无偏见，一致且与群集依赖性的替代休假群集估计器（LCOC）估计量。由于LCOC估计器将Kline，Saggio和Solvsten（2019）的剩余交叉估计器筑巢，因此两篇论文是统一的。提供了蒙特卡洛比较，以提供有关其有限样本特性的见解。然后将LCOC估计器应用于Angrist and Lavy（2009）对高中成就奖和Donohue III和Levitt（2001）对堕胎对犯罪影响的研究的研究。
Cluster standard error (Liang and Zeger, 1986) is widely used by empirical researchers to account for cluster dependence in linear model. It is well known that this standard error is biased. We show that the bias does not vanish under high dimensional asymptotics by revisiting Chesher and Jewitt (1987)'s approach. An alternative leave-cluster-out crossfit (LCOC) estimator that is unbiased, consistent and robust to cluster dependence is provided under high dimensional setting introduced by Cattaneo, Jansson and Newey (2018). Since LCOC estimator nests the leave-one-out crossfit estimator of Kline, Saggio and Solvsten (2019), the two papers are unified. Monte Carlo comparisons are provided to give insights on its finite sample properties. The LCOC estimator is then applied to Angrist and Lavy's (2009) study of the effects of high school achievement award and Donohue III and Levitt's (2001) study of the impact of abortion on crime.
