  We consider change-point latent factor models for high-dimensional time
series, where a structural break may exist in the underlying factor structure.
In particular, we propose consistent estimators for factor loading spaces
before and after the change point, and the problem of estimating the
change-point location is also considered. Compared with existing results on
change-point factor analysis of high-dimensional time series, a distinguished
feature of the current paper is that our results allow strong cross-sectional
dependence in the noise process. To accommodate the unknown degree of
cross-sectional dependence strength, we propose to use self-normalization to
pivotalize the change-point test statistic. Numerical experiments including a
Monte Carlo simulation study and a real data application are presented to
illustrate the proposed methods.
