Fall 2019 - ECON 835 G100
Class Number: 1018
Delivery Method: In Person
An introduction to econometric theory. Applications of econometric methods to both time series and cross-section data. Offered once a year.
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
a. Classical Assumptions
b. Finite Sample Properties
c. Hypothesis Testing
d. Generalized Least Squares
e. Maximum Likelihood
f. Large Sample Properties
g. Hypothesis Testing – Large Sample Results
2. Violations of the Classical Assumptions
a. Heteroskedasticity / Serial Correlation
b. Omitted Variable Bias
c. Instrumental Variables
d. Logit and Probit
- Weekly problem sets 20%
- Midterm exam 30%
- Final exam 50%
- Grades will be available via Canvas.
- Problem sets will be assigned approximately each week. You can work in groups and discuss the problems with each other, but every student must turn in his or her own work (e.g., you must understand every word of what you turn in). Consulting previous answer keys when doing an assignment is academic dishonesty, and I will treat it as such. The problem sets will be posted online when they are assigned. Late problem sets will not be accepted. Attendance at lecture is mandatory - I will not keep attendance records, but if you miss a lecture it is your responsibility to get the notes from a colleague.
Prerequisites: ECON 435 and ECON 798. 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.
MATERIALS + SUPPLIES:
The course is primarily based on lecture notes and other resources provided. There are many textbooks that can help supplement course material including the two above. Lecture notes will serve as the primary course material. Exam questions will be based on the lectures and problem set material.
Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. Vol. 5. New York: Oxford University Press, 2004.
Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008.
Other useful textbooks include A Guide to Econometrics, by Peter Kennedy (Wiley-Blackwell, 2008) and Econometric Analysis of Cross Section and Panel Data, by Jeffrey Wooldridge. These are available on reserve at the library.
I also really like Mastering Metrics, by Angrist and Pischke, which is similar to the required text of Mostly Harmless Econometrics but written for an audience with less econometrics background.
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.
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
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS