Fall 2020 - ECON 835 G100

Econometrics (4)

Class Number: 2213

Delivery Method: In Person


  • Course Times + Location:

    Mo 4:30 PM – 5:20 PM

    We 3:30 PM – 5:20 PM

  • Prerequisites:

    ECON 435 and ECON 798.



An introduction to econometric theory. Applications of econometric methods to both time series and cross-section data. Offered once a year.


Prerequisites: This course assumes that students enter with a basic knowledge of calculus, matrix algebra, probability and statistics.  Although part of the course is devoted to teaching students how to use statistical software, students are expected to be proficient at operating a PC.  If you feel you may be deficient in any of these areas, you should contact me as soon as possible. 

Description:  This is an introductory graduate level course in econometrics.  This course is designed to introduce students to the fundamental tools of econometrics. The primary goal of the course is to provide students an in-depth understanding of the classical linear regression model (CLRM) and the key identification assumptions.  Upon completing the course, successful students will be able to formulate econometric models, manage data and estimate regressions, and interpret results (sign, significance, and magnitude).  Successful students will understand finite and asymptotic properties of commonly used estimators, hypotheses testing, identification, estimation, accurate inference, linear, instrumental variables, logit/probit, and maximum likelihood regressions.

Topics (not necessarily in order presented in class): 

  1. The Classical Linear Regression Model
    1. Classical Assumptions
    2. Finite Sample Properties
    3. Hypothesis Testing
    4. Generalized Least Squares
    5. Maximum Likelihood
    6. Large Sample Properties
    7. Hypothesis Testing – Large Sample Results
  1. Violations of the Classical Assumptions
    1. Heteroskedasticity / Serial Correlation
    2. Omitted Variable Bias
    3. Instrumental Variables
    4. Logit and Probit



  • Assignments 70%
  • Lecture participation 5%
  • Final exam 25%


Grades will be available via Canvas. Assignments will be due approximately every other week. Late assignments will not be accepted. Attendance at lecture is mandatory.



We will rely on selected chapters from Bruce Hansen’s econometrics notes available for free online: https://www.ssc.wisc.edu/~bhansen/econometrics/.  I will post weekly lecture notes as well as other resources relevant to each lecture.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

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).