Parameter estimation and hypothesis testing in stationary vector time series

Statistical inference for stationary time series is often based on the maximum likelihood principle, i.e., the maximization of the (quasi) likelihood of observations derived on Gaussian assumptions, although no such distributional assumptions are made. In this paper, we define the disparity measure between spectral density matrices and introduce the minimum distance principle for parameter estimation and hypothesis testing in spectral analysis of stationary vector time series.