Recursive methods and their numerical application, in particular dynamic programming, have become the most important solution methods in modern macroeconomics. Today the areas of their application encompass almost all areas of macroeconomics, e.g. growth theory, monetary economics, social insurance, and fiscal policy. Despite their widespread use in research, they have not yet become part of the standard curriculum at European economic faculties. The course aims at closing this gap. It introduces dynamic programming from a theoretical perspective and discusses numerical solution methods. Subsequently, these methods are applied to solve the real business cycle model, which is the workhorse model of modern quantitative macroeconomics and constitutes the starting point to analyse, e.g. fiscal policy and social insurance in a dynamic economic environment.

Content

Numerical Basis: Optimisation, Root-Finding and Approximation Methods

  • Optimisation: One-Dimensional Methods, Newton, and Quasi-Newton Methods
  • Root-Finding: Newton Algorithm, Gauss-Seidel, and Gauss-Jacobi Method
  • Approximation Methods

Dynamic Programming

  • Theory: The Bellman Equation and the Principle of Optimality
  • Numerical Implementation: Discretization and Continuous Approximation of the Value Function with Focus on Value Function Iteration

Application: Numerical Solution of Real Business Cycle Model

  • Numerical Implementation: Solving the RBC Model by Value Function Iteration
  • Simulation: Calibration of Model Economy and Simulation of Time Paths
  • Interpretation of Numerical Results

Seminar Benefits

Researchers broaden their methodological expertise and improve programming skills.

Learning and Teaching Methods

Presentations and workshop sessions on programming, practical exercises with MATLAB

Target Groups

PhD students and postgraduates in macroeconomics as well as researchers in the financial sector.

Required Knowledge

Basic macro- and microeconomic theory as well as calculus.

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Date

19.09.2011 - 21.09.2011 | 9:00 - 17:30 Uhr