Introduction to Computational Economics Using Fortran - Kindermann, Fabian

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Résumé :
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Biographie:
Hans Fehr is Professor of Economics at the University of Wuerzburg. His previous roles have included Assistant Professor at the University of Tuebingen and Postdoctoral Researcher at Boston University. Professor Fehr's main research interests are in the field of quantitative public economics. His past work has focused on analyzing the economic consequences of population aging and various tax policy and social security reforms by means of computable general equilibrium models with overlapping generations. His research has been published in the European Economic Review the Journal of Economic Dynamics and Control, the Review of Economic Dynamics, and the Scandanavian Journal of Economics.; Fabian Kindermann is Assistant Professor of Economics at the University of Bonn. He was previously a Postdoctoral Researcher at Northwestern University and an Assistant Professor at the University of Wuerzburg. His research interests are in public economics and macroeconomics, where he uses quantitative macroeconomic models to shed light on the determinants of economi inequality, study the implications of inequality for the optimal design of tax and social security systems, and investigate issues in family economics. His work has been published in the European Economic Review, Review of Economic Dynamics, Journal of Economic Dynamics, and Control and Computational Economics.

Sommaire:
Part I: An Introduction to Fortran 90 and Numerical Methods; 1 Fortran 90: A Simple Programming Language; 2 Numerical Solution Methods; Part II: Computational Economics for Beginners; 3 The Static General Equilibrium Model; 4 Topics in Finance and Risk Management; 5 The Life Cycle Model and Intertemporal Choics; 6 The Overlapping Generations Model; 7 Extending the OLG Model; Part III: Advanced Computational Economics; 8 Introduction to Dynamic Programming; 9 Dynamic Macro I: Infinite Horizon Models; 10 Life-Cycle Choices and Risk; 11 Dynamic Macro II: The Stochastic OLG Model