Fall 2022 - ECON 483 D200
Selected Topics in Economics (3)
Class Number: 4070
Delivery Method: In Person
The subject matter will vary from term to term depending upon the interests of faculty and students.
This course is meant to teach applications of machine learning concepts to economic problems. In particular, the focus will be on program evaluation methods and their implementation via machine learning algorithms on R.
The course will cover:
- Standard econometric methods (OLS, GLS, IV regressions)
- Program evaluation methods: Average Treatment effects estimation, Difference-in-differences methods, Regression discontinuity designs
- Nonparametric methods: Kernel estimators, nearest neighbors
- Machine learning: Supervised methods for regression and classification and unsupervised methods
- Implementation of the methods in R
Topics: program evaluation, machine learning, R
- Term paper (30% of the final grade) 30%
- One paper presentation (20% of the final grade) 20%
- A midterm exam (20% of the final grade) 20%
- A term exam (30% of the final grade) 30%
Note: the assessment is subject to changes announced during the first week of classes
Grading Guidelines: Standard letter grades will be given the following interpretation
A+, A, A-: Excellent. Student has demonstrated knowledge of all or almost all course content and can apply this knowledge in unfamiliar or complex settings. Students regularly earning grades in this range are well-suited for honours and/or graduate study in economics. Students regularly earning a grade of A+ merit consideration for major undergraduate awards.
B+, B, B-: Good. Student has demonstrated knowledge of most course content and can apply this knowledge in familiar settings. Students regularly earning grades in this range are well-suited for the economics major or minor.
C+, C: Satisfactory. Student has demonstrated knowledge of basic course content. Students earning a grade in this range are qualified to take any economics course for which this course is a prerequisite.
C-: Marginally satisfactory. Student has demonstrated knowledge of most of the basic course content. Students earning this grade are marginally qualified to take any economics course for which this course is a prerequisite.
D: Marginally unsatisfactory. Student has demonstrated knowledge of some basic course content. Students earning this grade are not qualified to take economics courses for which this course is a prerequisite.
F: Unsatisfactory. Student has not demonstrated adequate knowledge of basic course content.
Technology requirements: All material will be posted on canvas, and lectures will be given in person.
- “Mostly harmless Econometrics” by Joshua D. Angrist and Jorn-Steffen Picshke
- “Introduction to statistical learning with R” by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani (available at https://www.statlearning.com/)
- “Causal inference: the mixtape”, by Scott Cunningham (free on https://mixtape.scunning.com/index.html)
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Department Undergraduate Notes:
Please note that, as per Policy T20.01, the course requirements (and grading scheme) outlined here are subject to change up until the end of the first week of classes.
Final exam schedules will be released during the second month of classes. If your course has a final exam, please ensure that you are available during the entire final exam period until you receive confirmation of your exam dates.Students requiring accommodations as a result of a disability must contact the Centre for Accessible Learning (CAL) at 778-782-3112 or firstname.lastname@example.org.
***NO TUTORIALS DURING THE FIRST WEEK OF CLASSES***
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html