Analyzing the Parameter Bias when an ARMAX Model is Fitted to Noise-Corrupted Data

Torsten Söderström and Umberto Soverini

October 2022

Abstract:

When an ARMAX model is fitted to noise-corrupted data using the prediction error method, biased estimates are obtained. The bias is examined, with emphasis on the situation when the system is almost non-identifiable. In contrast to the case of using an output error model, no general results on the size of the bias seem to apply.

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