Computational economics is an emerging branch of economic theory, which studies how the equilibrium can be efficiently computed. The search of a major realism in growth and business cycle models is pulling macroeconomists to consider more complex dynamic stochastic specifications, which requires the use of efficient numerical optimisation methods. The unifying theme of this proposal is the development of computational approaches for solving dynamic optimisation problems, and their application to general and realistic growth and business cycle models. Similarly to macroeconomic problems, business decision-makers face dynamic stochastic problems. An additional aim of this project is the application of these numerical methods to management decision, focusing on the robustness against unobserved shocks that might affect the decision outcome. This aim extends some previous work of the applicant on management decision in deregulated electricity markets. Furthermore, this proposal is concerned with the design of a robust procedure for calibrating parameters of macroeconomic models when the available empirical information is incomplete. The robustness in the calibration of the model is achieved by a worst-case approach. Worst-case modelling consists of essentially designing the best model that fits as much as possible to the available data in view of the worst-case scenario of unobserved decision variables. This is a key issue in many managerial problems.
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