  Block coordinate descent is an optimization technique that is used for
estimating multi-input single-output (MISO) continuous-time models, as well as
single-input single output (SISO) models in additive form. Despite its
widespread use in various optimization contexts, the statistical properties of
block coordinate descent in continuous-time system identification have not been
covered in the literature. The aim of this paper is to formally analyze the
bias properties of the block coordinate descent approach for the identification
of MISO and additive SISO systems. We characterize the asymptotic bias at each
iteration, and provide sufficient conditions for the consistency of the
estimator for each identification setting. The theoretical results are
supported by simulation examples.
