University of Maryland Baltimore County
We present a reparameterization of vector autoregressive moving average (VARMA) models that allows estimation of parameters under the constraints of causality and invertibility. The parameter constraints associated with a causal invertible VARMA model are highly complex. An m-variate VARMA(p; q) process contains (p+q)m2 + m(m+1)/2 parameters, which must be constrained to a complicated subset of the Euclidean space in order to guarantee causality, invertibility. The main result of the paper is a bijection from the constrained set to the entire Euclidean space.