Abstract
Abstract The asymptotical theoretical equivalence of finite order MA models with AR(∞) has invited users to take high order AR models as an intermediate in MA estimation with Durbin's method. But an investigation for small samples shows that sometimes more accurate MA models are found with a low order intermediate AR model; the best AR order depends on the characteristics of the process and on the number of observations. Hence, when no a priori knowledge about the AR order is available, it should be selected from the data. The Sliding Window Technique is introduced as an algorithm producing currently the most accurate MA models with a low Prediction Error. The intermediate AR order is adapted to the MA order under calculation.
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