Comparison of three Frisch methods for errors-in-variables identification

The errors–in–variables framework concerns static or dynamic systems whose input and output variables are affected by additive noise. Several estimation methods have been proposed for identifying dynamic errors–in– variables models. One of the more promising approaches is the so–called Frisch scheme. This report decribes three different estimation criteria within the Frisch context and compares their estimation accuracy on the basis of the asymptotic covariance matrices of the estimates. Some final numerical examples support the theoretical results and analyze the behaviour of the methods in case of finite number of data.