Fall 2022 - ECON 798 G100
Introduction to Mathematical Economics (4)
Class Number: 3647
Delivery Method: In Person
Applications of static optimization techniques, matrix algebra, differential and difference equations in economic models. Graded on a satisfactory/unsatisfactory basis.
This course reviews/introduces some important mathematical concepts and techniques commonly used in economic analysis and the core graduate theory courses. The main purpose is to brush up your math background so that you can focus on the economic intuition in your next classes.
The course will be taught on an intensive everyday basis in a two-week period beginning the last week of August. There will be both lectures emphasizing the theory and tutorials emphasizing problem solving. All incoming M.A. students must enroll. Entering Ph.D. students are also strongly encouraged to attend.
Topics to be covered include:
- Notation, logic, methods of proof, basic set theory.
- Elements of analysis
- Linear algebra and matrices
- Unconstrained and constrained single and multiple variable optimization
- Introduction to dynamic programming
- Probability theory and basic statistics
A final examination will be given. The course will be graded Pass/Fail
Required Text: There is no specific textbook for the course. Detailed class notes will be provided.
The books listed below cover most of the material (and much more) and could be used as references. Since the course will proceed rapidly and a lot of its contents is hopefully material you have covered before, you are encouraged to review what you can before the course begins.
Alpha Chiang and Kevin Wainwright, Fundamental Methods of Mathematical Economics, McGraw-Hill, 2005.
Stephen Glaister, Mathematical Methods for Economists, Blackwell Publishing, 1984
Eugene Silberberg, The Structure of Economics: A Mathematical Analysis, McGraw-Hill, 2000.
Carl Simon and Lawrence Blume, Mathematics for Economists, WW Norton and Co., 1994
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.
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.
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