Likelihood based uncertainty bounding in prediction error identification using ARX models: A simulation study

The purpose of this paper is to evaluate the reliability and finite sample properties of different likelihood based methods for constructing probabilistic parameter confidence regions in prediction error identification using ARX (Auto Regression with eXogenous inputs) models. The paper presents alternatives for the “classical” approach to constructing probabilistic confidence regions in prediction error identification.