Published Nov 21, 2022
This course is an introduction to prediction in economics using machine learning. Topics may include supervised and unsupervised learning, text analysis, regression trees, penalized regression, classification, random forest, neural network, and boosting methods.
Prereq: One of ECON 323, STAT 221, STAT 231, STAT 241; Honours students or Economics majors
One or more half-courses will be offered at different times as announced by the Department.
In this course, you will learn about data analysis techniques that are useful for complex models of economic behaviour in data-rich environments. We will discuss penalized regression and classification methods and how to examine the uncertainty in statistical estimators. Then we will turn to quasi-experimental methods with observed data and the way that penalized methods can be used to find the causal impact of policies. Finally, we will discuss dimension reduction via unsupervised learning and nonparametric methods for measuring relationships in high-dimensional data.
| Use and understand penalized regression and classification methods |
| Use penalized regression methods for causal inference with complex observational data |
| Use basic unsupervised learning methods for analysis of complex economic data |
Basic inference and resampling (2 weeks)
Regression and regularized regression estimators (2 weeks)
Classification (1 week)
Methods for causal inference (3 weeks)
Factors and principal components, text analysis (3 weeks)
Other nonparametric methods for complex data (1 week)
| Title / Name | Notes / Comments | Required |
|---|---|---|
| An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani | Available online | No |
| Component | Value |
|---|---|
| Assignments | 80% |
| Final project | 20% |
No assignment screening will be used in this course.
Academic integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check the Office of Academic Integrity for more information.]
Grievance: A student who believes that a decision affecting some aspect of their university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt, please be certain to contact the department’s administrative assistant who will provide further assistance.
Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for their actions. [Check the Office of Academic Integrity for more information.] A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate associate dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties, check Guidelines for the Assessment of Penalties.
Appeals: A decision made or penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes they have a ground for an appeal should refer to Policy 72, Student Appeals.
Note for students with disabilities: AccessAbility Services, located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AccessAbility Services at the beginning of each academic term.
Turnitin.com: Text matching software (Turnitin®) may be used to screen assignments in this course. Turnitin® is used to verify that all materials and sources in assignments are documented. Students' submissions are stored on a U.S. server, therefore students must be given an alternative (e.g., scaffolded assignment or annotated bibliography), if they are concerned about their privacy and/or security. Students will be given due notice, in the first week of the term and/or at the time assignment details are provided, about arrangements and alternatives for the use of Turnitin in this course.
It is the responsibility of the student to notify the instructor if they, in the first week of term or at the time assignment details are provided, wish to submit alternate assignment.