Course Description This course introduces the empirical and computational techniques necessary for numerically solving and estimating economic models. The course covers topics in numerical methods, such as optimization, function approximation, and Monte Carlo techniques, as well as topics in data exploration, visualization, and estimation. Emphasis will be placed on developing effective programming and research practices. The course is structured through a series of applications in such topics as macroeconomic fluctuations, industrial organization, and asset pricing. The course will be taught primarily in Python. Though helpful, no previous experience with computer programming is necessary for this class.
The full class syllabus can be found here.
- Class: Tuesdays and Thursdays, 3:30 - 4:50 PM in SHFE 103
- TA Session: Thursdays, 5-6 pm in Saieh 103
- Lecturer: Jeremy Bejarano, jbejarano@uchicago.edu
- Office Hours: Wednesday 5:30-6:20 PM in SHFE 203
- Teaching Assistant: Ari Boyarsky, aboyarsky@uchicago.edu
- TA Office Hours: Mondays, 10-11 am in the graduate commons (Saieh 201)
- Website: Canvas will be used for grades. Homework and notes will be posted on the course GitHub repo: https://github.com/jmbejara/comp-econ-sp19
There should be about 19 classes and 9 TA sessions (first Monday excluded). This means that we have 28 in-class sessions total before the reading period.
- Midterm Exam: Thursday, April 25. The exam will take place during class, 3:30-4:50 PM in SHFE 103.
- Final Exam:
- Normally scheduled final: Thursday, June 13th, 4-6pm, SHFE 103 (See the registrar’s site for more details.)
- Early final: June 5th, 12 - 2 PM, SHFE 203
- Very early final: May 30th, 6:30 - 8:30 PM, SHFE 141
Books Used
We will mostly used notes that I will provide. Sometimes we will cover lectures from the following sources:
- Lectures in Quantitative Economics, by Thomas J. Sargent and John Stachurski (QuantEcon)
- Python Data Science Handbook, by Jake VanderPlas (PDSH)
- Python for Data Analysis, 2nd Edition, by Wes McKinney (PDA)
Assignments
- Assignments must be turned in before midnight on the day that they are due. This means that you must submit to GitHub at 11:59pm or earlier on the due date (usually a Monday). Late assignments will not be accepted.
- Solutions to the homework will be posted in a separate GitHub repo, found here: https://github.com/econ-21410/hw-jbejarano1
The schedule is listed in the "lectures" directory. Each day of lecture has its own directory. The agenda for each particular day is described in the readme file within each day's directory. For example, the agenda for the first class can be found here and the agenda for the second day can be found here.
Important: the agenda for each day includes tasks that you should do before lecture to prepare Please be sure to go over the agenda and do all of the tasks that need to be done before you come to class. These tasks are important to do beforehand so that you will be able to fully participate in that day's lecture. For example, on the agenda for the first day you need to do the following:
- Familiarize yourself with the first homework assignment. This assignment is due on Monday, but you should start ASAP. In particular, you should install all the required software so that you can follow along with lectures.