Analysis and investigation of the conservativeness condition in the problem of parametric identification of distributed dynamic processes

Abstract

The problem of bias of OLS estimates arises when solving the problem of parametric identification of distributed dynamic processes. There are various possible solutions to this problem. If the time series is trend-stationary, then these may be “ostationation” methods, which are generally difficult to apply. It is possible to use dimensionality reduction methods, but in this case we will still get biased estimates. In our previous works, it was shown that the problem of biased estimates can be solved using the conservativeness condition. The aim of this work was to investigate the possibility of using the conservativeness condition to improve the quality of estimates of the parametric identification problem, as well as to compare these results with the solution of the problem, in the case of applying a filter to it, as well as ridge regression.