This dissertation is about optimal control of nonlinear dynamic econometric models. Two different types of problems are considered, namely nonlinear stochastic optimum control problems with one decision-maker, and nonlinear dynamic tracking games. Regarding the former an intensive research work on the OPTCON algorithm is performed resulting in a new version, OPTCON2. 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.
With regard to the second type of the optimum problems, research is carried out on nonlinear dynamic tracking games, resulting in the OPTGAME3 algorithm. The algorithm was programmed in C# and allows us to find a solution for the five game strategies, one cooperative (Pareto optimal) and four non-cooperative game types (the Nash and Stackelberg games for the open-loop and feedback information patterns), within a finite planning horizon dynamic tracking game.
An additional focus of this work is the application of the new algorithms to the problems of real economies. Moreover, this work includes specification and estimation of macroeconometric models. The aim is to study different and, in particular, 'optimal' fiscal and monetary policies, especially in the presence of different economic shocks. According to the two different types of problems considered, the single country models and models of monetary unions are analyzed. The latter allow us to model the conflicts between policy makers from different countries, who primarily pursue their own national interests and do not care (in non-cooperative solutions) about the spillovers of their actions to other countries.