Estimation of the Polynomial Matrices of Vector Moving Average Processes

In practice the number of nonzero parameters in the polynomial matrices of vector time series models is often small. Consequently, estimation of fully parameterized models, particularly those containing many variables, can be computationally quite demanding. In this article we present methods for estimating parameter values and for identifying the nonzero elements in vector moving average models which are at least an order of magnitude faster than existing maximum likelihood procedures