Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates
Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates
复制标题
具有许多协变量的线性回归模型中的异方差鲁棒推理
DOI：
--
复制
发表时间：
2018
影响因子：
3.7
通讯作者：
Koen Jochmans
中科院分区：
文献类型：
--
作者：
Koen Jochmans
摘要：我们考虑对异方差和存在许多控制变量的线性回归模型具有鲁棒性的推理。当控制变量的数量以与样本量相同的速率增加时，协方差矩阵的通常异方差鲁棒估计是不一致的。因此，基于这些估计量的测试即使在大样本中也是尺寸扭曲的。本文提出了一种可替代的协方差矩阵估计，补充了Cattaneo， Jansson和Newey最近的工作。我们为我们的方法提供了高级的条件，以提供（渐近的）尺寸正确的推理，并为三种特殊情况提供了更原始的条件。最后给出了仿真结果，并给出了联合溢价推理的实证说明。本文的补充材料可在网上获得。
Abstract We consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroscedasticity-robust estimators of the covariance matrix are inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator for such a setting is presented that complements recent work by Cattaneo, Jansson, and Newey. We provide high-level conditions for our approach to deliver (asymptotically) size-correct inference as well as more primitive conditions for three special cases. Simulation results and an empirical illustration to inference on the union premium are also provided. Supplementary materials for this article are available online.