Summer 2026 - ECON 484 D100
Selected Topics in Economics (3)
Class Number: 2087
Delivery Method: In Person
Overview
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Course Times + Location:
May 11 – Aug 10, 2026: Thu, 2:30–5:20 p.m.
Burnaby
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Instructor:
Kevin Laughren
klaughre@sfu.ca
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Prerequisites:
To be determined by the instructor subject to approval by the department chair.
Description
CALENDAR DESCRIPTION:
The subject matter will vary from term to term depending upon the interests of faculty and students.
COURSE DETAILS:
Prerequisites: ECON 333
Description:
We discern the difference between economics problems that require (causal) estimation and those that require prediction accuracy. We apply R statistical packages to economic data to develop prediction models. We adapt models from R packages to applied economic settings, tune model performance through cross-validation, and explain prediction models to a non-technical audience through visuals and writing. Students choose an economic prediction problem and author their own economic research paper including identifying a data source, programming a prediction model, and writing up results.
Course Learning Outcomes:
Students build on their ability to analyze and interpret economic data. Students learn to critically assess economic arguments, assumptions, and evidence. Students learn to use written and graphical methods to communicate economic insights. Students learn to use economic concepts to understand real-world human activity and public policy.
Topics:
1. Introduction to statistical learning
2. Review of linear regression
3. Classification methods
4. Tree-based and ensemble methods
5. Resampling and cross-validation methods
6. Model boosting
7. Adaptive sampling
8. Model and variable selection
9. Prediction policy problems
10. Causal inference with machine learning models
Grading
- Assignments 30%
- Exercises (in-class) 30%
- Term Project 40%
NOTES:
Two assignments worth 15% each require you to solve a prediction problem in economics with coding and a written explanation for a non-technical audience.
Each week includes an in-class exercise that involves coding and a brief written explanation, the ten best weekly scores will be counted for 3% each toward your grade.
The term paper is worth 40% and requires you to choose a prediction problem in economics, find a data source, program a prediction model, and write a detailed explanation of the methodology and results. Interim deadlines for topic choice, first draft, and peer editing are worth 10%. The final draft is worth the remaining 30%.
Materials
RECOMMENDED READING:
In addition to the readings below, there will be applied economics papers that are posted to the course website and available for free through the SFU Library website.
Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. (2015). Prediction Policy Problems. American Economic Review, 105 (5): 491–95. http://dx.doi.org/10.1257/aer.p20151023
James, Witten, Hastie, and Tibshirani, (2021). An Introduction to Statistical Learning with Applications in R. 2nd ed. (corrected 2023). Springer. Available for download at https://www.statlearning.com/
Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends® in Machine Learning, 11(1), 1-96. https://doi.org/10.1561/2200000070
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 caladmin@sfu.ca.***NO TUTORIALS DURING THE FIRST WEEK OF CLASSES***
Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.
To learn more about the academic disciplinary process and relevant academic supports, visit:
- SFU’s Academic Integrity Policy: S10-01 Policy
- SFU’s Academic Integrity website, which includes helpful videos and tips in plain language: Academic Integrity at SFU
RELIGIOUS ACCOMMODATION
Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.