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We propose a novel recursive system identification algorithm for linearautoregressive systems with skewed innovations. The algorithm is based on thevariational Bayes approximation of the model with a multivariate normal priorfor the model coefficients, multivariate skew-normally distributed innovations,and matrix-variate-normal - inverse-Wishart prior for the parameters of theinnovation distribution. The proposed algorithm simultaneously estimates themodel coefficients as well as the parameters of the innovation distribution,which are both allowed to be slowly time-varying. Through computer simulations,we compare the proposed method with a variational algorithm based on thenormally-distributed innovations model, and show that modelling the skewnesscan provide improvement in identification accuracy.

Approximate Recursive Identification of Autoregressive Systems with Skewed Innovations