Mean-Structure and Autocorrelation Consistent Covariance Matrix Estimation
提出一种非参数估计量，用于非平稳时间序列的渐近协方差矩阵估计，无需搜索变化点或估计趋势，计算高效，并配有自动最优带宽选择器，适用于变化点检测和趋势置信带构建。
We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally efficient. It is, therefore, useful in many statistical procedures, for example, CPs detection and construction of simultaneous confidence bands of trends. Empirical studies on four stock market indices are also discussed.