EC315: Computational Economics

This course introduces students to a broad set of computational methods used by economists.

The approach is hands-on: start with an economic problem, select an appropriate numerical technique for analysing it, apply the technique to the problem, and present your findings. The programming language of choice is Python.

Although the numerical techniques should be those indicated in the Programme Structure, the economic topics may be adapted to the interest of the audience, and the aim is to have economic examples also when presenting the basics of Python. For example, the programming concept of recursion and for loops can be illustrated with the Solow growth model; linear quadratic programming can be developed while talking about dynamic monopoly with durable goods, or a Stackelberg game.

Computational techniques are illustrated in lectures along with the economic models, and complemented with guided exercises during the classes.

Prerequisites: Intermediate microeconomics and macroeconomics, knowledge of multivariate calculus and linear algebra.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: A mid-session take-home assignment (40%) and a final take-home assignment (60%).

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is suited to those who would like a hands-on approach to learn a broad set of computation methods used by economists. If you are interested in the use of coding and models within the study of economics, this course is for you.

• Command the basics of Python for scientific computing
• Establish a computational strategy to solve an economic model
• Use visualization techniques for presenting computational findings
• Master numerical methods for economic analysis
• Have a working knowledge of the Github platform.

The design of this course is guided by LSE faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.