Signal dimension estimation in BSS models with serial dependence

Many modern multivariate time series datasets contain a large amount of noise, and the first step of the data analysis is to separate the noise channels from the signals of interest. A crucial part of this dimension reduction is determining the number of signals. In this paper we approach this problem by considering a noisy latent variable time series model which comprises many popular blind source separation models. We propose a general framework for the estimation of the signal dimension that is based on testing for sub-sphericity and give examples of different tests suitable for time series settings. In the inference we rely on bootstrap null distributions. Several simulation studies are used to demonstrate the performances of the tests in different time series settings.