SPRING 2016
BUEC 333: Statistical Analysis of Economic Data
David Jacks, djacks@sfu.ca
Office hours: MW 2:30 – 3:30 pm, WMC 3669
COURSE DESCRIPTION
This course will introduce you to econometrics, the statistical analysis of economic data. Our
focus will be on linear regression, the most common method for analyzing the relationship
between two or more variables. The main things you will learn in this class are how to apply
regression methods to economic data and to interpret the results of econometric analysis. You
will also get some experience using statistical software and establish a foundation for further
study of econometrics.
The course website can be found at www.sfu.ca/~djacks/courses/BUEC333/index.html
Here, you can access homework assignments, past exams, sample questions, tutorial questions,
and my webnotes.
INFORMATION
Contact: I highly encourage you to attend my office hours. Answering more than the simplest of
questions by email is generally not feasible given the nature of the course material. This is also
the best way for me to get to know you on a more personal basis, an important part of any good
letter of reference.
Readings: The recommended text for the course is Studenmund’s Using Econometrics, 6th
edition. On the next page, you will find an approximate schedule for the lectures and readings.
Updates to the reading list will be posted on the course website as the term progresses.
Top Hat: Students will be required to register with Top Hat, a web-based “clicker” application
(https://app.tophat.com/register/student/). During lectures, questions will be asked relating to the
course material. You will need to provide an answer, using a compliant device like a cellphone,
laptop, or tablet. Your answers will be collected and analyzed immediately. In this way, lecture
content can then be adapted to respond to the immediate feedback and help you in your learning.
This will require that you show up, pay attention, and play by the rules of the game.
Tutorials: The tutorials are a venue for you to ask questions; discuss the assignments, lectures,
and readings; and review for the exams. Questions will be assigned before every tutorial; these
questions will be highly indicative of the exam material.
Assignments: There will be two graded assignments consisting of exercises in data analysis. The
assignments will be posted on the course website. Feel free to work in groups of up to four
people. The assignments will get you doing some actual data analysis using Stata (available for
download at http://www.stata.com/order/new/edu/gradplans/student-pricing/ - the cheapest
option is the six-month license for Small Stata at $38). Assignments are due at the beginning of
the indicated lecture; no late assignments will be accepted nor will assignments sent via email.
When you submit your assignment, please submit one assignment per group and write down the
names of all people who have worked on the assignment. Each individual in the group will
receive the same grade for the assignment. Collaboration is encouraged; copying answers is not.
If the assignment of group/person A appears to be identical to that of group/person B, but A's
name is not on B's assignment or vice-versa, then both A and B will receive an automatic zero on
the assignment.
Lab: The Department PC lab is WMC 2506. There will be a TA in the lab for 6 hours each week:
Tuesdays, 10:30 am to 1:30 pm and Tuesdays, 2:00 pm to 5:00 pm. Outside of these hours, you
can use Stata at home.
Grading: Your grade will be based on your performance in Top Hat (10%), assignments (20%),
a midterm (35%), and a final (35%). The two exams will be composed of multiple choice and
short answer questions. NB: you may opt out of the midterm (no need to get my permission) and
only take the final as I will automatically use the higher of {the average of your midterm & final
exam, your final exam}. As a consequence of this policy, the final will be cumulative.
Absences: Missing a lecture or an exam due to a medical or family emergency does happen. But
you need to give me advanced notice and the more the better. If you are sick, you must submit
this form: http://www.sfu.ca/~djacks/healthcareproviderstatement.pdf
I will then phone or visit your health care provider to verify.
Academic dishonesty: I take cheating very seriously and will not hesitate to fail you (F), fail you
with dishonor (FD), or have you expelled from the university if possible. I operate under a zero
tolerance policy: if you are found cheating on any single component of your participation grade,
assignments, or exams, I will assess a zero for the entirety of your work. Finally, SFU has clear
policies on academic dishonesty which you should review:
http://www.sfu.ca/policies/teaching/t10-02.html
COURSE SCHEDULE
January 11: Introduction to course (NO TUTORIALS)
January 13: Review of statistics (Chapter 17)
January 18: Review of statistics (Chapter 17)
January 20: Review of statistics (Chapter 17)
January 25: Review of statistics (Chapter 17)
January 27: Review of statistics (Chapter 17)
February 1: Introduction to regression analysis (Chapter 1)
February 3: Introduction to regression analysis (Chapter 1)
February 15: Ordinary least squares (Chapter 2)
February 17: Goodness of fit (Chapter 2)
February 22: Regression analysis in practice (Chapter 3)
February 24: The classical linear regression model (Chapter 4)
February 29: The classical normal linear regression model (Chapter 4)
March 2: The classical normal linear regression model (Chapter 4) (ASSIGNMENT #1 DUE)
March 7: MIDTERM (NO TUTORIALS)
March 9: To be announced
March 14: Hypothesis testing in linear regression models (Chapter 5)
March 16: Hypothesis testing in linear regression models (Chapter 5)
March 21: Specification issues (Chapters 6 & 7)
March 23: Specification issues (Chapters 6 & 7)
March 28: EASTER BREAK (NO CLASS AND NO TUTORIALS)
March 30: Specification issues (Chapters 6 & 7)
April 4: Multicollinearity (Chapter 8)
April 6: Serial correlation (Chapter 9)
April 11: Heteroskedasticity (Chapter 10) (ASSIGNMENT #2 DUE)
April 15: FINAL EXAM from 8:30 – 11:30 am (C9001)