@TechReport{	  it:2022-009,
  author	= {Torsten S{\"o}derstr{\"o}m and Umberto Soverini},
  title		= {Analyzing the Parameter Bias when an {ARMAX} Model is
		  Fitted to Noise-Corrupted Data},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Systems and Control},
  year		= {2022},
  number	= {2022-009},
  month		= oct,
  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.}
}