Robust systems theory applications to macroeconomic stabilization problems

This research involves the use of robust systems theory tools to deal with identification, model uncertainty and optimization in macroeconomic stabilization problems, in order to avoid Lucas Critique.;This is accomplished by using time domain based robust semi-blind identification to obtain models of macroeconomic systems of interest, as well as a bound on the identification error. By using past inputs to reflect the effect of the system initial conditions and convex relaxations, the original nonconvex model identification and (in)validation problems are reduced to jointly convex problems.;Linear Fractional Transformations (LFT) are used to optimally design a robust controller when model uncertainties and performance objectives can be treated in structural uncertainty framework. This LFT structure enables to achieve robust performance under simultaneous model uncertainty and performance objectives requirements.;These results are extended to time varying models by developing an LPV framework for semi-blind model identification and validation. The potential of this approach is illustrated by designing a robust controller for a macroeconomic stabilization problem, i.e., inflation targeting problems. Since the LPV framework can incorporate the macroeconomic dynamics changes for different equilibrium points and schedule controllers accordingly by utilizing linearly varying parameters, it answers the famous Lucas critique.