An Introduction to Computational Macroeconomics (Tokyo 2022)
- Lecturer: John Stachurski
- Lecture times: Wednesdays 3 and 4 periods (13:00-16:40).
- Start date: 8/6/2022
Overview
This course provides a short but fast-moving introduction to computational modeling in macroeconomics and finance. Topics include numerical methods and their application to workhorse models in macroeconomics, such as Markov chains, asset pricing problems and dynamic programming.
Notifications
This is the course homepage. Any new information or resources for the course will be posted below. Please check this page at least once per week.
Topics
- Scientific programming in Python
- Foundations of numerical methods
- Job search
- Fixed point theory in vector space
- Finite Markov chains
- Finite Markov decision processes
- Applications: optimal savings and investment
- Recursive decision processes
- Recursive preferences
- State-dependent dynamic programming
- Optimal savings in a general setting
- Euler equation methods
Resources
Primary source material:
- Dynamic Programming: Volume 1 (John Stachurski and Thomas J. Sargent) available here.
Warning: These notes are still being edited! Please print sections sequentially throughout the course, rather than all at once.
Secondary reading material:
- Abstract dynamic programming by Dimitri Bertsekas
Programming resources:
Assessment
- One programming assignment
- One exam at the end of the course