Spectral Density Estimation for Nonstationary Data With Nonzero Mean Function
提出一种基于子采样技术的非参数谱密度估计新方法，适用于几乎周期相关的非平稳时间序列，无需对数据进行去均值处理，并通过模拟和实际经济数据验证了其有效性。
We introduce a new approach for nonparametric spectral density estimation based on the subsampling technique, which we apply to the important class of nonstationary time series. These are almost periodically correlated sequences. In contrary to existing methods, our technique does not require demeaning of the data. On the simulated data examples, we compare our estimator of spectral density function with the classical one. Additionally, we propose a modified estimator, which allows to reduce the leakage effect. Moreover, in the supplementary materials, we provide a simulation study and two real data economic applications. Supplementary materials for this article are available online.