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A vector autoregressive model of order one with multivariate generalized scaled t-distributed innovations is considered here. The object is to estimate the parameters of the proposed model by using the well-known maximum likelihood estimation method. The maximum likelihood estimation method is performed by using the expectation–conditional maximization algorithms (ECM and ECME) to accelerate the basic EM algorithm. The proposed methods are also compared to a quasi-Newton well-known method, named BHHH in terms of the rate of convergence as well as the precision and validity of the obtained estimates, by implementing some numerical simulations. At last, the outcomes are used to fit the model and predict for a real data set.

On accelerating the EM-based algorithms for the VAR(1) models with multivariate generalized scaled t -distributed innovations