Fall 2020 - ECON 333 D100

Statistical Analysis of Economic Data (4)

Class Number: 2433

Delivery Method: In Person


  • Course Times + Location:

    Th 8:30 AM – 11:20 AM

  • Instructor:

    Bertille Antoine
    1 778 782-4514
  • Prerequisites:

    ECON 103 or 200; ECON 105 or 205; ECON 233 or BUS (or BUEC) 232 or STAT 270; MATH 157; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics.



An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.


Description:  The course will introduce you to the statistical analysis of economic data, also called econometrics. We will focus on linear regression, which is by far the most common method for analyzing the relationship between two or more variables: we will emphasize both the use and interpretation of linear regression methods. Topics covered in this course include: hypothesis testing, specification, multicollinearity, and heteroskedasticity. You will also get some experience using the statistical software R, and establish a foundation for further econometric study. 

Additional important information:

  • You need to be available during the scheduled class time.
  • Lectures will be delivered remotely and asynchronously through Canvas. Synchronous components of this course include: a weekly quizz, a Q&A session to review and discuss assigned readings and lecture notes, a tutorial session, and office hours. There will be weekly graded assignments consisting of both exam-type questions and computer exercises. The assignments will be posted on the course webpage. You are expected to work independently on the assignments. Cheating of any kind will result in at least a failing grade in the course.
  • The computer exercises will involve some real data analysis using the statistical software R: R can be downloaded for free to use on your own computer; it is also currently installed on the lab computers. The use of R is essential to this course; since lab computers may not be used during the session, it is important that you install the software as soon as possible to ensure that it woks properly. It is also strongly suggested that you become familiar with the software before the start of the semester. A good place to start: follow the steps described at http://swirlstats.com/students.html (Links to an external site.)
  • The exam-type exercises will involve statistical computations and derivations. I encourage you to prepare for ECON 333 by reviewing material from your introductory statistics course: e.g. probability distributions, sampling distributions, hypothesis testing.
  • Details regarding lab hours will be announced during the first week of class. It is your responsibility to make yourself available for the lab hours to get extra help on the computer exercises.
  • It is your responsibility to check the course webpage regularly for up-to-date information regarding assignments, deadlines and reading materials.


  • Participation 5%
  • Quizzes 30%
  • Assignments 20%
  • Midterm exam 20%
  • Final exam 25%



Introduction to Econometrics - Fourth Edition by James Stock and Mark Watson

ISBN: 978-0-13-446199-1

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.

Starting Fall 2020, final exam schedules will be released in October. This will allow students to avoid enrollment conflicts, and will significantly reduce instances of exam hardship. If your course has a final exam, please ensure that you are available during the final exam period December 9 - 20 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.


Registrar Notes:


SFU’s Academic Integrity web site 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


Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).