Fall 2019 - ECON 832 G100
Computational Methods in Economics (4)
Class Number: 3142
Delivery Method: In Person
Overview
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Course Times + Location:
Sep 3 – Dec 2, 2019: Fri, 9:30 a.m.–12:20 p.m.
Burnaby
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Instructor:
Jasmina Arifovic
arifovic@sfu.ca
1 778 782-5603
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Prerequisites:
ECON 802, 807 or 808, or with the approval of the instructor.
Description
CALENDAR DESCRIPTION:
The first part of the course will focus on dynamic optimization problems, with an emphasis on dynamic programming. Applications may include growth, business cycles, monetary and fiscal policy, and optimal contracts. The second part of the course will focus on models of learning and bounded rationality. Genetic and stochastic approximation algorithms will be studied. Applications may include the stability of rational expectations equilibria, the evolution of institutions and social conventions, and models of robust control and Knightian uncertainty.
COURSE DETAILS:
This class will focus on a number of topics in the area of computational economics. These will include studying learning, adaptation, machine learning and agent-based economics in a variety of economic environments. After the initial introduction to the area and basic training in how to use and apply these methods, the class will cover a number of different topics. These include, but are not limited to, models of oligopolistic competition, voluntary contribution mechanisms, game theoretic environments, auctions, financial markets, growth, evolution of payments systems, monetary policy etc. After we cover the basic core of the methodology, and learn how to apply it, the course readings and applications will be guided by students’ research interest. We will also study how we can test the predictions of these models through the analysis of real world data as well as through experiments with human subjects.
We are going to use Python, general programming language, for our class work, problem sets, and projects. Python has recently become widely used in many areas of economics, and data processing and analysis. In-class introductory instruction will be provided, as well as couple of lectures that will go over specific applications. Students will learn how to use it through simple class assignments. There will also be a continuous support in terms of handling students’ questions.
If students are already comfortable and have a working knowledge of some other software or programming language, such as Matlab or R, they, of course, will be able to use those to complete class work.
No prior knowledge of programming is required as the students will obtain in class instruction.
Grading
- Midterm exam 35%
- Class assignments 25%
- Class discussion 10%
- Term paper 30%
Materials
REQUIRED READING:
There will be a list of readings for the class which can be accessed through SFU library's electronic journals collection and working papers that are available on line.
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
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS