An algorithm for computing the asymptotic fisher information matrix for seasonal SISO models

The paper presents an algorithm for computing the asymptotic Fisher information matrix of a possibly seasonal single-input single-output (SISO) time-series model. That matrix is a block matrix whose elements are basically integrals of rational functions over the oriented unit circle. The procedure makes use of the autocovariance or the cross-covariance function of two autoregressive processes based on the same noise. The algorithm also works when the input variable is omitted, the case of a seasonal ARMA model. Copyright 2004 Blackwell Publishing Ltd.