Heteroskedasticity-consistent covariance matrix estimation:white's estimator and the bootstrap
Heteroskedasticity-consistent covariance matrix estimation:white's estimator and the bootstrap
复制标题
异方差一致的协方差矩阵估计：怀特估计器和引导程序
DOI：
10.1080/00949650108812077
复制
发表时间：
2001
影响因子：
1.2
通讯作者：
Spyros G. Zarkos
中科院分区：
文献类型：
--
作者：
Francisco Cribari‐Neto;Spyros G. Zarkos
本文研究了怀疑异方差时线性回归模型中普通最小二乘估计的协方差矩阵的估计问题。我们对常用的White估计器进行了蒙特卡罗模拟。并对不同的自助方案进行了实证研究。我们的研究结果表明，当样本量不是很大时，White估计量可能会有相当大的偏差，通过自举法进行的偏差校正效果不佳，并且在同方差和异方差下，加权自举估计量往往比White估计量及其变体显示更小的偏差。我们的研究结果还表明，在设计矩阵中存在（潜在的）有影响的观测值对异方差一致估计器的有限样本性能起着重要作用。
This paper considers the issue of estimating the covariance matrix of ordinary least squares estimates in a linear regression model when heteroskedasticity is suspected. We perform Monte Carlo simulation on the White estimator, which is commonly used in. empirical research, and also on some alternatives based on different bootstrapping schemes. Our results reveal that the White estimator can be considerably biased when the sample size is not very large, that bias correction via bootstrap does not work well, and that the weighted bootstrap estimators tend to display smaller biases than the White estimator and its variants, under both homoskedasticity and heteroskedasticity. Our results also reveal that the presence of (potentially) influential observations in the design matrix plays an important role in the finite-sample performance of the heteroskedasticity-consistent estimators.