Prediction in Locally Stationary Time Series
针对具有平滑变化趋势的局部平稳过程，提出一种高维协方差矩阵估计量，并基于此统计量构建非平稳时间序列的一致预测方法，不依赖自回归模型且不要求趋势消失。
We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a simulation study and a financial indices study. Supplementary materials for this article are available online.