Fundamental Limitations on the Variance of Estimated Parametric Models

In this technical note fundamental integral limitations are derived on the variance of estimated parametric models, for both open and closed loop identification. As an application of these results we show that, for multisine inputs, a well known asymptotic (in model order) variance expression provides upper bounds on the actual variance of the estimated models for finite model orders. The fundamental limitations established here give rise to a dasiawater-bedpsila effect, which is illustrated in an example.