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Statistically/Computationally efficient estimation of non-Gaussian autoregressive processes

A new technique for the estimation of autoregressive filter parameters of a non-Gaussian autoregressive process is proposed. The probability density function of the driving noise is assumed to be known. The new technique is a two-stage procedure motivated by maximum likelihood estimation. It is computationally much simpler than the maximum likelihood estimator and does not suffer from convergence prroblems. Computer simulations indicate that unlike the least squares or linear prediction estimators, the proposed estimator is nearly eifficient, even for moderately Sized data records.