Spectral Density Estimation for Nonstationary Data With Nonzero Mean Function.

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 , we provide a simulation study and two real data economic applications. for this article are available online. [ABSTRACT FROM AUTHOR]