  This paper analyzes identifiability properties of structural vector
autoregressive moving average (SVARMA) models driven by independent and
non-Gaussian shocks. It is well known, that SVARMA models driven by Gaussian
errors are not identified without imposing further identifying restrictions on
the parameters. Even in reduced form and assuming stability and invertibility,
vector autoregressive moving average models are in general not identified
without requiring certain parameter matrices to be non-singular. Independence
and non-Gaussianity of the shocks is used to show that they are identified up
to permutations and scalings. In this way, typically imposed identifying
restrictions are made testable. Furthermore, we introduce a maximum-likelihood
estimator of the non-Gaussian SVARMA model which is consistent and
asymptotically normally distributed.
