An instrumental variable method for model order identification

The paper describes a simple instrumental variable method for identifying the structure of a wide class of time-series models that can be applied to single input-single output and multivariable systems using either discrete or continuous-time series models.

Abstract

The paper describes a simple instrumental variable method for identifying the structure of a wide class of time-series models. The method is aimed at providing a parametrically efficient (parsimonious) model structure which will lead to a combination of low residual error variance, i.e. a good explanation of the data, and low parametric estimation error variance (as measured by some norm associated with the covariance matrix of the estimation errors). It can be applied to single input-single output and multivariable systems using either discrete or continuous-time series models. It can also function as a recursive (on-line) test for reduction in model order.