This dissertation considers optimal control of nonlinear dynamic stochastic econometric models. The research is based on an existing algorithm, OPTCON, which was developed by Matulka and Neck (1992) for solving such kind of optimum problems. However, so far OPTCON has been severely limited by being based on very restrictive assumptions about the information available to the decision-maker. The development from open-loop control only (the basic OPTCON algorithm, also named OPTCON1) to the inclusion of passive learning or open-loop feedback control, where the estimates of the parameters are updated in each time period due to the idea of the Kalman Filter, results in the OPTCON2 algorithm.
The OPTCON2 algorithm delivers approximate numerical solutions to optimum control problems with a quadratic objective function for nonlinear econometric models with additive and multiplicative (parameter) uncertainties. The algorithm was programmed in C# and allows for deterministic and stochastic control, the latter with open-loop and passive learning (open-loop feedback) information patterns. In addition to the theoretical developments the applications of the new algorithm to some macroeconomic models are performed. For this purpose two existing models are used, MacRae and Abel, as well as two new models, SLOVNL and SLOVL, which were developed for the purposes of this dissertation. The application part of my dissertation demonstrates the usefulness of the new version of the algorithm. The applications show the applicability and the convergence of the OPTCON2 algorithm.
Furthermore, by comparing the open-loop feedback and the open-loop optimal solutions for the nonlinear SLOVNL model and the linear SLOVL model it is shown that open-loop feedback controls give better results in the majority of the cases investigated for the OPTCON2 algorithm.