AR processes with non-Gaussian asymmetric innovations

We consider the problem of modeling non-Gaussian correlated signals by autoregressive models with skew exponential power innovations. Generalized moments and maximum likelihood estimators of the parameters are proposed and large sample properties are established. Finite sample behavior of the estimators is studied via Monte Carlo simulations. An application to real data is considered.

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