Spring 2023 - ECON 335 D100

Cause and Effect in Economic Data (3)

Class Number: 3280

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 10:30 AM – 12:20 PM
    AQ 3149, Burnaby

  • Corequisites:

    ECON 333.

Description

CALENDAR DESCRIPTION:

Provides an introduction to statistical methods used to analyze causal questions and evaluate policies. Discusses various approaches to drawing causal inferences from observational data. Students who have taken ECON 480 first may not then take this course for further credit.

COURSE DETAILS:

Economists and other social scientists often seek to measure the real-world effects of policy. More generally, we often want to assess the real-world effects of some potential “cause” on an “outcome.” For example, does a university degree increase future earnings? Does public health insurance make people healthier? Do environmental regulations reduce pollution? Do stricter capital requirements change bank lending behaviour?

 

This course will introduce you to the statistical and econometric methods that applied researchers use to answer causal questions like these. We will develop ideas in the potential outcomes framework and apply them to data using the R software package. Topics may include randomized experiments, regression discontinuity, matching, difference-in-differences, and instrumental variables. There will be regular graded assignments that will give you hands-on experience with data analysis in R. By the end of the course, you will learn how to critically evaluate statements about causal relationships, and apply a variety of methods to draw causal inferences of your own using R.

Topics:

  1. Review of Statistical Methods
  2. The Potential Outcomes Framework
  3. Randomized Experiments
  4. Introduction to Regression
  5. Instrumental Variables
  6. Regression Discontinuity Designs
  7. Difference-in-Differences
  8. Fixed Effects and Standard Errors

 

Grading

  • Participation 10%
  • Assignments 25%
  • Midterm Exam 30%
  • Final Exam 35%

Materials

REQUIRED READING:

  1. D. Angrist and J.-S. Pischke “Mastering ‘Metrics: The Path from Cause to Effect,” Princeton University Press (2014).

RECOMMENDED READING:

  1. Wickham and G. Grolemund “R for Data Science: Import, Tidy, Transform, Visualize, and Model Data,” O'Reilly Media (2017).

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

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