piwik-script

Intern
    Chair of Public Finance

    Advanced Computational Economics

    Contents & Objectives:

    This course provides state of the art techniques for quantitative macroeconomic research. Students will learn about the most prominent models in this field and familiarize with the relevant literature. At the end of the semester, they are guided to develop their own ideas and conduct policy analysis using the techniques acquired in class. The course will consist of a series of lectures and tutorials and is divided into three parts:

    1. Basic concepts of programming in FORTRAN and standard numerical methods are reviewed.

    2. Introduction of solution techniques for dynamic programming problems.

    3. Dynamic programming techniques are applied to stochastic growth and life-cycle models.

    Course Structure

    Week  
    1-2 FORTRAN 90: A simple programming language
    3-4 Numerical solution methods: Nonlinear equation systems, function minimization, function approximation and interpolation
    5-6 Introduction to dynamic programming
    7-9 Stochastik growth models
    10-12 Stochastic life cycle models

    Prerequisites:

    Note that it is not required to have completed the undergraduate class "Computational Economics", but it is expected to have already basic programming experience. A background in high-level programming languages such as Matlab, Python or R is sufficient.

    Course materials:

    Hans Fehr & Fabian Kindermann (2018): Introduction to Computational Economics using FORTRAN, Oxford University Press.

    Grading:

    There will be five graded assignments which are solved in groups throughout the semester.