QuantEcon Summer Course in Quantitative Economics
The Summer Course in Quantitative Economics 2022 will be run by QuantEcon in partnership with the African School of Economics (ASE), and the Institute of Statistics and Applied Economics (ENSEA). The target audience for the course is talented young economists from ASE and ENSEA who want to learn about data science and computer-driven economic modeling.
The course will provide students with an introduction and exposure to key tools in quantitative economics that are in high demand in academic economics and industry.
The workshop will run Monday-Friday July 11-15 and Monday-Friday July 18-22.
The lead instructor and academic coordinator for the program is Thomas J. Sargent, co-founder of QuantEcon, Professor of Economics at New York University, and 2011 Nobel Laureate in Economic Sciences.
How will the Workshops Work?
Due to the large number of enrolments, lectures will be pre-recorded rather than live. We will also provide assignments and a discourse forum where you can ask questions and get answers from instructors and your peers. New videos and assignments will be distributed through Google Classroom, starting from July 11th.
Students will require access to a computer.
You will need to sign up to the Google Classroom using this invitation link. This is where we will distribute materials for the workshop.
We recommend using Google Colab for running notebooks that contain lecture material and Python code. We will provide instructions for using Google Colab, as well as for installing Python on your own computer (which is optional, not required).
You can now sign up to the Workshop Discourse Forum We strongly encourage you to use the forum to ask questions and get answers from instructors and peers.
The language of instruction will be English. The programming language used in the course will be Python.
The course will cover selected topics from
- Foundations of programming in Python
- Data engineering with Pandas
- Economic dynamics
- Linear algebra
- Optimization, filtering and dynamic programming
- Search and matching models
- Asset pricing
- Bayesian and frequentist statistics
- Quantitative economic history
- High-performance computing with Python
Background reading on these topics can be found in the following lecture series:
Mode of Instruction
Students will be provided with materials to help them prepare for classes and solidify understandings of earlier classes.
Students are encouraged to form study groups to foster working in teams (a valuable skill in both industry and government and academia). We hope that study groups can include students with mixtures of skills and mathematical and statistics backgrounds.
Participants will learn how to connect economic modeling problems to numerical implementations in Python using clear, efficient and effective code. Participants will be able to apply basic software engineering principles to organize, share and collaborate on coding problems.
These skills are in high demand for economists and data scientists at leading tech companies and universities around the world.
The course material will be challenging. Ideal candidates will already have had at least some exposure to probability, linear algebra and principles of economics, up to the level of a good undergraduate economics or computer science program. Programming experience will be helpful but is not a prerequisite.
Organizers of the program can be reached through