CMStatistics 2020: Start Registration
View Submission - CMStatistics
Title: Adaptive estimation for evolutionary high-dimensional time series factor models and a test for static factor loadings Authors:  Weichi Wu - Tsinghua University (China) [presenting]
Zhou Zhou - University of Toronto (Canada)
Abstract: The estimation and testing of a class of high-dimensional non-stationary time series factor models with evolutionary temporal dynamics are considered. In particular, the entries and the dimension of the factor loading matrix are allowed to vary with time while the factors and the idiosyncratic noise components are locally stationary. We propose an adaptive sieve estimator for the span of the varying loading matrix and the locally stationary factor processes. A uniformly consistent estimator of the effective number of factors is investigated via eigenanalysis of a non-negative definite time-varying matrix. A high-dimensional bootstrap-assisted test for the hypothesis of static factor loadings is proposed by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. We examine our estimator and test via simulation studies and real data analysis.