A0404
Title: Testing for white noises in multivariate locally stationary functional time series
Authors: Lujia Bai - Tsinghua University (China) [presenting]
Weichi Wu - Tsinghua University (China)
Holger Dette - Ruhr-Universitaet Bochum (Germany)
Abstract: Multivariate locally stationary functional time series provides a flexible framework for modelling complex data structures exhibiting both temporal and spatial dependencies while allowing for a time-varying data-generating mechanism. A specialized Portmanteau test tailored for assessing white noise assumptions is introduced for multivariate locally stationary functional time series without dimension reduction. The Gaussian approximation result is derived from the Gaussian approximation result for the kernel-weighted second-order functional time series, which is of independent interest. A simple bootstrap procedure is proposed to implement the test where the limiting distribution can be non-standard or even does not exist. Through theoretical analysis and simulation studies, the efficacy and adaptability of the proposed portmanteau test are demonstrated in detecting departures from white noise assumptions in multivariate, locally stationary functional time series.
