Observer Based Control with Nonlinear Macroeconometric Models

This paper is concerned with the use of low order linear models to develop controls for large nonlinear macroeconometric models. This contrasts with the usual approach to this class of problems of using nonlinear optimization with the nonlinear model, or Linearization of the model and the use of linear quadratic control techniques. The use of output injection from the nonlinear model into the low order model allows it to learn about the nonlinear model's behavior. The low order model acts as an observer of the larger nonlinear model and is used as the basis of developing control policies for the nonlinear model. As the observer is linear, then linear control techniques can be used with the model. Such an approach is less computationally intensive than nonlinear optimization, especially with a low order observer. It also saves explicit linearizing of the nonlinear model. Further, as the observer learns the behavior of the nonlinear model, then it need not be as concerned with many of the issues, such as the solution of forward looking behavior of economic actors, that are of concern to the nonlinear model.