Carnegie Mellon University

Computational Methods for Economics

Course Number: 47809

This is a course in the basic tools of numerical analysis that can be used both to assess the quantitative implications of economic theory and to derive theoretical results of economic models without analytical solutions. While the most examples will come from macroeconomics, the generality with which the techniques will be presented in this course will make them applicable to a wide range of fields like econometrics, financial economics, marketing, game theory, political economy, and contract theory. To enable efficient application of numerical tools, this course endeavors to explain not only when and how to use various numerical algorithms but also how and why they work; in other words, the intention of the course is to open up some black boxes and provide the students with a versatile tool set. The course will cover basic topics in numerical methods such as systems of equations, optimization problems, functional approximation and numeric integration. The course will then focus on solving dynamic problems using various approaches to differential equations and dynamic programming. Advanced topics will include perturbation methods and dynamic stochastic games. Students are expected to have basic knowledge of calculus and linear algebra, as well as Matlab or any other programming language.

Degree: PhD
Concentration: Economics
Academic Year: 2022-2023
Semester(s): Mini 4
Required/Elective: Elective
Units: 6


Lecture: 100min/wk and Recitation: 50min/wk