This analysis explores robust designs for an applied macroeconomic discrete-time LQ tracking model with perfect state measurements. We develop a procedure that reframes the tracking problem as a regulator problem that is then used to simulate the deterministic, stochastic LQG, H-infinity, multiple-parameter minimax, and mixed stochastic/H-infinity control, for quarterly fiscal policy. We compare the results of the five different design structures within a closed-economy accelerator model using data for the United States for the period 1947-2012. When the consumption and investment tracking errors are more heavily emphasized, the H-infinity design renders the most aggressive fiscal policy, followed by the multiple-parameter minimax, mixed, LQG, and deterministic versions. When the control tracking errors are heavily weighted, the resulting fiscal policy is initially more aggressive under the multi-parameter specification than under the H-infinity and mixed designs. The results from both weighting schemes show that fiscal policy remains more aggressive under the robust designs than the deterministic model. The simulations show that the multiparameter minimax and mixed designs provide a balancing compromise between the stochastic and robust methods when the worst-case concerns can be primarily limited to a subset of the state-space.

This analysis formulates an approach for converting minimax LQ (linear-quadratic) tracking problems into LQ regulator designs, and develops a Matlab application program to calculate an H-infinity robust control for discrete-time systems with perfect state measurements. It uses simulations to explore examples in financial asset decisions and utility input purchasing, in order to demonstrate the method. The user is allowed to choose the parameters, and the program computes the generalized Riccati Equation conditions for the existence of a saddle-point solution. Given that it exists, the program computes a minimax solution to the linear quadratic (LQ) soft-constrained game with constant coefficients for a general scalar model, and also to a class of matrix systems. The user can set the bound to achieve disturbance attenuation.