Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown Form
研究了在异方差和/或自相关形式未知的情况下，如何估计最小二乘回归系数的协方差矩阵，给出了White估计量一致性的简化证明，并提出了一个改进小样本性质的替代估计量。
This paper deals with the problem of estimating the covariance matrix of the least-squares regression coefficients under heteroskedasticity and/or autocorrelation of unknown form. We consider an estimator proposed by White [17] and give a relatively simple proof of its consistency. Our proof is based on more easily verifiable conditions than those of White. An alternative estimator with improved small sample properties is also presented.