Automatic Lag Selection in Covariance Matrix Estimation
提出一种非参数方法，自动选择用于计算异方差自相关一致协方差矩阵的自协方差数量，并证明其渐近等价于均方误差最优选择，蒙特卡洛模拟显示该方法表现尚可但存在尺寸扭曲。
We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.