A0930
Title: Testing for the independence of long-range dependent time series based on distance correlation
Authors: Annika Betken - University of Twente (Netherlands) [presenting]
Abstract: The concept of distance correlation is applied for testing the independence of long-range dependent time series. For this, we establish a non-central limit theorem for stochastic processes with values in an L2-Hilbert space. This limit theorem is of general theoretical interest that goes beyond the considered context. For the purpose of testing the independence of time series, it provides the basis for deriving the asymptotic distribution of the distance covariance of subordinated Gaussian processes. Depending on the dependence on the data, the standardization and the limit of distance correlation vary. In any case, the limit is not feasible, such that test decisions are based on a subsampling procedure. We prove the validity of the subsampling procedure and assess the finite sample performance of a hypothesis test based on the distance covariance.
