The class will be a mixture of theory and programming. The first half or 2/3 of this course is about learning the computational tools used in modern macroeconomics, and applying the programming tools. Then, based on the previous knowledge we discuss some macroeconomic modeling and research.
- Instructor: Peifan Wu, [email protected]
- Postdoc @ VSE, 19' Ph.D. @ NYU Stern (Econ), 13' BSc @ Tsinghua (Econ/Finance/Math), learned some programming in high school (China NOI, ACM/ICPC, etc)
- Office Hours: Tuesdays 9:00am-10:00am, Vancouver Time (Pacific Daylight Time)
- Prerequisites: One of ECON 301, ECON 304 and one of ECON 302, ECON 305 and one of ECON 303, ECON 306. Not very strict though.
- A little programming experience is very valuable, but you will be able to fill in the blanks with some hard work.
- We'll go through some introductory contents in the class
- Textbook: No Textbook. We will follow a small subset of QuantEcon Python lectures, and I'll post notes under this repository
- Learning Environment: Regular (virtual) attendance and (virtual) interaction are expected. The lecture sessions will be recorded.
- Course Materials will be online, mostly under this Github repository
- Course stream through Canvas
- Slides, Notes, Communications, Announcements, and Grades: all through Canvas
This will be based on projects and problems sets, with no exams.
- 5-6 Problem Sets throughout the semester (computational, or math): 65%
- Final Project: 35%, a computational project. You'll have a great deal of freedom to implement a small final project using what we learn.
While I will show programs in Python and we will use several QuantEcon Python lectures and I strongly recommend Python for this course, you don't have to stick to Python, as you can use Julia, Matlab, or other languages. Stata doesn't count -- it won't be sufficient for our class.
Write me an email if you are not going to use Python/Julia/Matlab, and specify what you will use.
In submitting your computational problem sets:
- Python: Jupyter notebook (.ipynb) is ideal
- Julia: Jupyter notebook
- Matlab: Source code (.m) with your results (figures, variables, etc)
The principle is to show your source code that I can replicate your results, or at least I can read your code.
(subject to change: we might not able to cover everything)
- Math Review
- Matrices and Basic Linear Algebra
- Basic Optimization
- Probability
- Programming Introduction
- Using Python, Jupyter Hubs, (Github)
- Tools and Techniques
- Linear Algebra
- Finite Markov Chains
- Linear State Space Models
- Discrete State Dynamic Programming
- Single Agent Models
- Job Search I: The McCall Search Model
- (Job Search II: Search and Separation)
- Optimal Growth I: The Stochastic Optimal Growth Model
- The Idea of General Equilibrium
- Endogenous interest rate and wage
- The Real Business Cycle Model
- Topics
- Firm Dynamics: Hayashi and others
- Macro-Finance (Asset Pricing Part): Lucas model, CCAPM
- Fiscal Policy: Tax distortion
- Monetary Policy (maybe not, too hard)
- Incomplete Markets and Heterogeneous Agents
During this pandemic, the shift to online learning has greatly altered teaching and studying at UBC, including changes to health and safety considerations. Keep in mind that some UBC courses might cover topics that are censored or considered illegal by non-Canadian governments. This may include, but is not limited to, human rights, representative government, defamation, obscenity, gender or sexuality, and historical or current geopolitical controversies. If you are a student living abroad, you will be subject to the laws of your local jurisdiction, and your local authorities might limit your access to course material or take punitive action against you. UBC is strongly committed to academic freedom, but has no control over foreign authorities (please visit http://www.calendar.ubc.ca/vancouver/index.cfm?tree=3,33,86,0 for an articulation of the values of the University conveyed in the Senate Statement on Academic Freedom). Thus, we recognize that students will have legitimate reason to exercise caution in studying certain subjects. If you have concerns regarding your personal situation, consider postponing taking a course with manifest risks, until you are back on campus or reach out to your academic advisor to find substitute courses. For further information and support, please visit: http://academic.ubc.ca/supportresources/freedom-expression
The academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work. Violations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment or exam and more serious consequences may apply if the matter is referred to the President’s Advisory Committee on Student Discipline. Careful records are kept in order to monitor and prevent recurrences.