A0344
Title: Mean stationarity test in time series: A signal variance-based approach
Authors: Kin Wai Chan - The Chinese University of Hong Kong (Hong Kong) [presenting]
Hon Kiu To - The Chinese University of Hong Kong (Hong Kong)
Abstract: The inference of mean structure is an important problem in time series analysis. Various tests have been developed to test for different mean structures, for example, the presence of structural breaks, and parametric mean structures. However, many of them are designed for handling specific mean structures, and may lose power upon violation of such structural assumptions. We propose a new mean stationarity test built around the signal variance. The proposed test is based on a super-efficient estimator which could achieve a convergence rate faster than $\sqrt{n}$. It can detect the non-constancy of the mean function under serial dependence. It is shown to have promising power, especially in detecting hardly noticeable oscillating structures. The proposal is further generalized to test for smooth trend structures and relative signal variability. A real-data application on global land surface temperature data is presented. This research was partially supported by General Research Fund 14304420, 14306421, and 14307922 provided by the Research Grants Council of HKSAR.
