Adv. Topics in Comp Economics

3 Credits · Level: Graduate

Computational economics comprises the application of mathematics and computational technology to the solution of economic problems. The topics covered in Econ 5902, Advanced Topics in Computational Economics, will vary from semester to semester. They may include numerical methods beyond those covered in Econ 5901, Computational Economics, such as interpolation and approximation; Monte Carlo methods and stochastic simulation; dynamic programming and other optimization techniques; perturbation methods; iterative methods for solution of sparse linear systems; and solution of partial differential equations. Similarly, Econ 5902 exposes students to more advanced computational techniques, such as symbolic programming; parallel processing and concurrency; a range of software development tools; and technical typesetting software such as LaTeX. The course will typically use additional packages and languages employed by economists beyond basic Python, such as as Dynare, EViews, Julia, R, and Wolfram Mathematica. The economic applications covered by the course will vary and may focus on microeconomic or macroeconomic computational problems. Assessments may comprise midterm and final exams, problem sets and projects involving coding, and presentations.