Instructor: Contact: Place: Semester:

Georg D¨ urnecker [email protected] University of Mannheim Spring 2017

Computational Economics Syllabus

Course description: This course provides an introduction to numerical tools for computing equilibria in economic models. The main emphasis is on learning the methods and their practical implementation. The course will require students to use standard computer programming languages (such as Matlab, Fortran or C). We study a variety of topics, including projection methods, approximation of functions and stochastic processes, sparse grid methods, numerical quadrature, root-finding, homotopy methods, calibration, parallel programming (OpenMP, MPI) and GPGPU computing. Moreover, we explore a number of applications in labor search, inequality and business cycles to illustrate the practical use of the methods presented in the course. Prerequisites: E700-E703, E801-E806 Method (hours per week): Lecture (2) + practical class (1) Time and Location: This is a half-semester course. We meet each week on Monday from 12:00-13:30, Wednesday from 15:30-17:00 and Thursday from 13:45-15:15 in 308 (L9,7). Class start: April 3, 2017 (End: June 1, 2017). Examination: The course will be evaluated through a series of exercises. Exercises can be done in groups of up to 2 students. The last exercise has to be done individually. Readings: Students are recommended to consult the following textbooks • Fabio Canova: Methods for Applied Macroeconomic Research • Burkhard Heer – Alfred Maussner: Dynamic General Equilibrium Modelling • Ken Judd: Numerical Methods in Economics • Lars Ljunqvist – Thomas J. Sargent: Recursive Macroeconomic Theory • Jerome Adda – Russell Cooper: Dynamic Economics • William Press et al: Numerical Recipes, The Art of Scientific Computing • Miranda – Fackler: Applied Computational Economics and Finance

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Computational Economics

of applications in labor search, inequality and business cycles to illustrate the practical use ... Method (hours per week): Lecture (2) + practical class (1).
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