  Statistical identification of possibly non-fundamental SVARMA models requires
structural errors: (i) to be an i.i.d process, (ii) to be mutually independent
across components, and (iii) each of them must be non-Gaussian distributed.
Hence, provided the first two requisites, it is crucial to evaluate the
non-Gaussian identification condition. We address this problem by relating the
non-Gaussian dimension of structural errors vector to the rank of a matrix
built from the higher-order spectrum of reduced-form errors. This makes our
proposal robust to the roots location of the lag polynomials, and generalizes
the current procedures designed for the restricted case of a causal structural
VAR model. Simulation exercises show that our procedure satisfactorily
estimates the number of non-Gaussian components.
