论文标题
本地固定时间序列的预测
Prediction in locally stationary time series
论文作者
论文摘要
我们开发了一个局部固定过程的高维协方差矩阵的估计器，其趋势平稳，并使用此统计数据来得出非平稳时间序列中的一致预测指标。与当前可用的方法相反，此处开发的预测因子不依赖于自回归模型，并且不需要消失的趋势。新方法的有限样本特性通过仿真研究和金融指数研究说明。
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 non-stationary 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.
