A necessary and sufficient condition for asymptotic independence of discrete Fourier transforms under short- and long-range dependence

Abstract

Let $\{X_t\}$ be a stationary time series and let $d_T(\lambda)$ denote the discrete Fourier transform (DFT) of $\{X_0,\ldots,X_{T-1}\}$ with a data taper. The main results of this paper provide a characterization of asymptotic independence of the DFTs in terms of the distance between their arguments under both short- and long-range dependence of the process $\{X_t\}$. Further, asymptotic joint distributions of the DFTs $d_T(\lambda_{1T})$ and $d_T(\lambda_{2T})$ are also established for the cases $T(\lambda_{1T}- \lambda_{2T})=O(1)$ as $T\to\infty$ (asymptotically close ordinates) and $|T(\lambda_{1_T}-\lambda_{2_T})|\to\infty$ as $T\to\infty$ (asymptotically distant ordinates). Some implications of the main results on the estimation of the index of dependence are also discussed.