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Semester 2

Programming and Numerical Methods for Economics (ECNM10115)

Subject

Economics

College

CAHSS

Credits

20

Normal Year Taken

3

Delivery Session Year

2023/2024

Pre-requisites

Visiting students must have completed the equivalent of at least 4 semester-long Economics courses at grade B or above for entry to this course. This MUST INCLUDE courses in: Intermediate Macroeconomics (with calculus); Intermediate Microeconomics (with calculus); Probability & Statistics. If macroeconomics and microeconomics courses are not calculus-based, then, in addition, Calculus (or Mathematics for Economics) is required at grade B or above.

Course Summary

Programming for Economics is designed to teach the essential skills for computational work in economics. Many economic models cannot be solved analytically but are easy to solve and simulate on a computer. Students who take this course will learn the basics of how to program, how to clean data and calculate basic statistics, and the numerical methods necessary to solve economic models, and estimate them from the data (including, but not limited to, approximating and simulating Markov chains, root finding, constrained optimization, and interpolation, and value function iteration).

Course Description

By the end of the course, the students should feel comfortable with programming basics, data management and analysis, and numerical solution methods to many common economic models. Students will also learn how to estimate the parameters of these models using real world data. Programming for Economics will include weekly lab sessions and tutorials to reinforce lectures, and weekly problem sets to give students concrete experience (as well as a portfolio of code they can show to potential employers). A student who takes this course will learn new programming tools and language, perform data analysis, solve economic models numerically, and estimate economic models. The student will also gain competencies such as programming skills, critical mathematical and computational thinking, and work collaboratively in code projects. At the end of the course, the student should be well prepared for future work on a computational undergraduate dissertation, have the computational knowledge for future employment in private companies and research centers, or for further graduate study.

Assessment Information

Written Exam 0%, Coursework 100%, Practical Exam 0%

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