System error variance tuning in state-space models

The multivariate dynamic regression model is a particular specification of the dynamic linear model. For this model, we propose a recursive equation for the estimation of the system error variance matrix. The solution can be used when more observation axe available at each state of the system. In these cases, the algorithm allows to define a recursive procedure for the estimate of both the state vector (the regression coefficients) and the other hyperparameters of the model. The performances of the proposed method are evaluated by means of Monte Carlo experiments.