Dynamic General Equilibrium Modeling

Offers an introduction to the use of numerical methods for solving dynamic general equilibrium models Provides algorithms and program codes on an accompanying website This is the 3rd completely revised and extended edition

Autorentext

Burkhard Heer is a professor of public economics at the University of Augsburg, Germany. Previously, he was professor of economics at the Universities of Bolzano, Bamberg, and Innsbruck. Burkhard Heer received his PhD in economics from the University of Cologne, Germany, in 1996. He was visiting scholar at various institutions including Georgetown University, Stanford University, Fordham University at New York, University of Quebec at Montreal, University Pompeu Fabra in Barcelona and the Federal Reserve Bank at St. Louis. Burkhard Heer is also affiliated with the Center for Economic Studies (CESifo), Munich, and NETSPAR, Tilburg. His research interests include public economics and macroeconomics. He also published a Springer textbook on Public Economics.

Alfred Maußner is a professor of empirical macroeconomics at the University of Augsburg, Germany. Previously he was professor of economics at the Universities of Bamberg and Cologne. He received his PhD in Economics at the University of Erlangen-Nuremberg in 1984. He was visiting scholar at the University of Athens, Georgia and the University of California in Los Angeles. His research interests include economic growth, business cycles, and computational methods. He is author/co-author of several textbooks published in German.

Inhalt
Part I: Representative Agent Models: Basic Models.- Perturbation Methods: Framework and Tools.- Perturbation Methods: Solutions.- Perturbation Methods: Model Evaluation and Applications.- Weighted Residuals Methods.- Simulation-Based Methods.- Discrete State Space Value Function Iteration.- Part II: Heterogenous Agent Models: Computation of Stationary Distributions.- Dynamics of the Distribution Function.- Overlapping Generations Models with Perfect Foresight.- OLG Models with Uncertainty.- Part III: Numerical Methods: Linear Algebra.- Function Approximation.- Differentiation and Integration.- Nonlinear Equations and Optimization.- Difference Equations and Stochastic Processes.