An efficient method for the estimation of multivariate moving averge models

Abstract

Durbin's (1959) efficient method for the estimation of univariate moving average models is generalized to the vector case. Strong consistency and asymptotic normality of the estimator is proved. A simulation experiment is performed to illustrate the behaviour of the method in finite samples.

Keywords:

• asymptotic normality
• long autoregresion
• multivariate moving average model
• strong consistency