| Jason DeBacker | |
|---|---|
| jason.debacker@moore.sc.edu | |
| Office | 427B DMSB |
| Office Hours | T 2:45-4:45pm, Th 9:00-11:00am |
| GitHub | jdebacker |
- Meeting day/time: T,Th 1:15-2:30pm, DMSB, Room 121
- Office hours also available by appointment
This course is designed to introduce PhD students to software applications and computational techniques to make them productive researchers. Students will be exposed to leading open source software pack-ages (Python, R, Julia) and techniques for numerical computing and data analysis. The course will be taught through the application of these software packages and methods to economic research in applied microeconomics and macroeconomics.
- You will learn how to use software to increase your research productivity including:
- TeX
- git
- Python
- R
- Julia
- You will learn to write your own estimators and use packages written by others for:
- Error-components models
- Maximum likelihood estimators
- Maximum score estimators
- Reduced form estimators such as regression discontinuity design
- Simulated method of moment estimators
- You will learn computational methods to:
- Solve dynamic programming problems
- Perform Monte Carlo simulations
- Bootstrap standard errors
- You will learn how to use software tools to gather data from the web.
- You will learn coding and collaboration techniques such as:
- Best practices for Python coding (PEP 8)
- Writing modular code with functions and objects
- Creating clear docstrings for functions
- Collaboration tools for writing code using Git and GitHub.com.
Grades will be based on the categories listed below with the corresponding weights.
| Assignment | Points | Percent |
|---|---|---|
| Problem Sets | 90 | 90% |
| Class Participation | 10 | 10.0% |
| Total points | 100 | 100.0% |
- Homework: I will assign 9 problem sets throughout the semester.
- You must write and submit your own computer code, although I encourage you to collaborate with your fellow students. I DO NOT want to see a bunch of copies of identical code. I DO want to see each of you learning how to code these problems so that you could do it on your own.
- Problem set solutions, both written and code portions, will be turned in via a pull request from your private GitHub.com repository which is a fork of the class master repository on my account. (You will need to set up a GitHub account if you do not already have one.)
- Written solutions must be submitted as pdf documents or Jupyter Notebooks.
- Problem sets will be due on the day listed in the Daily Course Schedule section of this syllabus (see below) unless otherwise specified. Late homework will not be graded.
| Date | Day | Topic | Due |
|---|---|---|---|
| Aug. 24 | Th | Work Flow, Productivity Software | |
| Aug. 29 | T | Work Flow, Productivity Software | |
| Aug. 31 | Th | Python/OOP | PS #1 |
| Sept. 5 | T | Python/OOP | |
| Sept. 7 | Th | Functions, Optimizers, Root-Finders | |
| Sept. 12 | T | Functions, Optimizers, Root-Finders | PS #2 |
| Sept. 14 | Th | Functions, Optimizers, Root-Finders | |
| Sept. 19 | T | Functions, Optimizers, Root-Finders | |
| Sept. 21 | Th | Conditionals/Loops | |
| Sept. 26 | T | Conditionals/Loops | PS #3 |
| Sept. 28 | Th | Visualization | |
| Oct. 3 | T | Visualization | PS #4 |
| Oct. 5 | Th | Econometrics in Python and R | |
| Oct. 10 | T | Econometrics in Python and R | PS #5 |
| Oct. 12 | Th | Econometrics in Python and R | |
| Oct. 17 | T | Econometrics in Python and R | |
| Oct. 19 | Th | No class, Fall Break | |
| Oct. 24 | T | Web scraping/APIs to gather data | |
| Oct. 26 | Th | Web scraping/APIs to gather data | PS #6 |
| Oct. 31 | T | Web scraping/APIs to gather data | |
| Nov. 2 | Th | Web scraping/APIs to gather data | |
| Nov. 7 | T | Dynamic Programming and intro to Julia | |
| Nov. 9 | Th | TBD | |
| Nov. 14 | T | Dynamic Programming and intro to Julia | PS #7 |
| Nov. 16 | Th | Dynamic Programming and intro to Julia | |
| Nov. 21 | T | Dynamic Programming and intro to Julia | |
| Nov. 23 | Th | No class, Thanksgiving | |
| Nov. 28 | T | Simulation Methods | |
| Nov. 30 | Th | Simulation Methods | PS # 8 |
| Dec. 5 | T | Simulation Methods | |
| Dec. 7 | Th | Simulation Methods | |
| Dec. 14 | Th | No Final Exam - project due | PS #9 |
- QuantEcon
- Notes on Machine Learning & Artificial Intelligence by Chris Albon
If you have any condition, such as a physical or learning disability, which will make it difficult for you to carry out the work as I have outlined it or which will require academic accommodations, please notify me through email AND in person with the appropriate documentation within the first two weeks of the course. Please also copy the course TA to this message.