An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
提出一类新的异方差和自相关一致协方差矩阵估计量，采用向量自回归预白化的核估计方法，证明其一致性、收敛速度及渐近均方误差性质，蒙特卡洛模拟显示预白化能有效减少偏差、改善置信区间覆盖率和t统计量的过度拒绝问题。
This paper considers a new class of heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators. The estimators considered are prewhitened kernel estimators with vector autoregressions employed in the prewhitening stage. The paper establishes consistency, rate of convergence, and asymptotic truncated mean squared error (MSE) results for the estimators when a fixed or automatic bandwidth procedure is employed. Conditions are obtained under which prewhitening improves asymptotic truncated MSE. Monte Carlo results show that prewhitening is very effective in reducing bias, improving confidence interval coverage probabilities, and rescuing over-rejection of t-statistics constructed using kernel-HAC estimators. On the other hand, prewhitening is found to inflate variance and MSE of the kernel estimators. Since confidence interval coverage probabilities and over-rejection of t-statistics are usually of primary concern, prewhitened kernel estimators provide a significant improvement over the standard non-prewhitened kernel estimators.