This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.

In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real macroeconomic data and show that VARMA models forecast macroeconomic variables more accurately than VAR models.

In this paper we propose bootstrapping Hannan and Rissanen (1982) estimators in stationary invertible VARMA models, with known order (p, q). Although we consider bootstrapping such models under the echelon form parameterization, the results we derive herein remain valid to other alternative identification issues. In particular, we shall exploit the theoretical developments stated in Dufour and ...

A useful class of partially nonstationary vector autoregressive moving average (VARMA) models is considered with regard to parameter estimation. An exact maximum likelihood (EML) approach is developed on the basis of a simple transformation applied to the error-correction representation of the models considered. The employed transformation is shown to provide a standard VARMA model with the imp...

Strong consistency and asymptotic normality of a Gaussian quasi-maximum likelihood estimator for the parameters causal, invertible, identifiable vector autoregressive-moving average (VARMA) model are established in an indirect way. The proof is based on similar results much wider class VARMA models with time-dependent coefficients, hence context non-stationary heteroscedastic time series. For t...