Open as website here

It is strongly suggested to get GitHub's student developer pack which gives you lots of free stuff (and even access to the AI GitHub Copilot ). Optionally, select Watch at the top of this repository to be alerted to all file changes

See here for more details, a course outline, and grading information.

1. Due Midnight PST on January 17th - Problem Set 0
   • This is a pass/fail problem set (i.e., hand it in you pass!) just to ensure you have setup your computer properly
2. Due Midnight PST on January 30th - Problem Set 1
3. Due Midnight PST on February 6th - Problem Set 2
4. Due Midnight PST on February 13th - Problem Set 3
5. Due Midnight PST on TBD - Problem Set 4
6. Review February 26th - Midterm Practice Problems
7. February 28th - MIDTERM EXAM IN CLASS
8. Due Midnight PST on TBD - Problem Set 4
9. Due Midnight PST on TBD - Problem Set 5
10. Due Midnight PST on TBD - Problem Set 6
11. Reviewing TBD - Final Practice Problems

All problem set solutions should be submitted on Canvas directly as a .ipynb file with instructions embedded.

See here for links to all materials.

1. January 8th - Introduction, course overview, VSCode Intro to Julia
   • Lecture Notes: Course Overview and Computational Environment
   • References:
     • Julia Overview
     • First sections of Julia by Example
     • Essentials
     • Fundamental Types
2. January 10th - Introduction to Fixed Points and Geometric Series and start of Asset Pricing applications
   • Lecture Notes: Geometric Series, Fixed Points, and Asset Pricing
   • References:
     • Later sections of Julia by Example
     • Geometric Series for Elementary Economics
3. January 15th - Continue on applications of Fixed Points and Geometric Series
   • Lecture Notes: Geometric Series, Fixed Points, and Asset Pricing
   • References:
     • Later sections of Julia by Example
     • Geometric Series for Elementary Economics
4. January 17th - Start Dynamics and Introduction to Growth Models
   • Lecture Notes: Deterministic Dynamics and Introduction to Growth Models and contraction mappings in Geometric Series, Fixed Points, and Asset Pricing
   • References:
     • Dynamics in One Dimension
5. January 22nd - Solow Model and Stability
   • Lecture Notes: Deterministic Dynamics and Introduction to Growth Models
   • References:
     • Dynamics in One Dimension
6. January 24th - Finish Malthusian Model and start AR(1) and Stochastic Dynamics
   • Lecture Notes: Deterministic Dynamics and Introduction to Growth Models and Stochastic Dynamics, AR(1) Processes, and Ergodicity
   • References:
     • AR1 Processes
     • LLN and CLT
     • Continuous State Markov Chains
7. January 29th - Stochastic Dynamics, AR(1), ARMA, and Ergodicity
   • Lecture Notes: Stochastic Dynamics, AR(1) Processes, and Ergodicity
   • References:
     • AR1 Processes
     • LLN and CLT
     • Continuous State Markov Chains
8. January 31st - Finish Nonlinear Stochastic Dynamics and Start Wealth Inequality
   • Lecture Notes: Stochastic Dynamics, AR(1) Processes, and Ergodicity and Wealth Distribution, Firm Dynamics, and Inequality
   • References:
     • Wealth Distribution Dynamics
9. February 5th - Firm Dynamics, Lorenz Curves and Power Laws

• Lecture Notes: Wealth Distribution, Firm Dynamics, and Inequality
• References:
  • Wealth Distribution Dynamics

10. February 7th - Review of PS1 and PS2 in class
11. February 12th -
12. February 14th -
13. February 19th - SPRING BREAK
14. February 21st - SPRING BREAK
15. February 26th - Midterm Review
16. February 28th - Midterm
17. March 4th -
18. March 6th -
19. March 11th -
20. March 13th -
21. March 18th -
22. March 20th -
23. March 25th -
24. March 27th -
25. April 1st - STATUTORY HOLIDAY
26. April 3rd -
27. April 8th -
28. April 10th -

For the computational environment, it is strongly suggested to install VS Code as well as Jupyter. Familiarity with those tools will make it easier for future work in industry or as research assistants. See here to setup your environment.

While we will be using a computer for simulations and numerical solutions to our models, this is an economics course. If you have the prerequisites, then do not be scared off by the programming requirements! Relative to many classes, the coding will be kept simple. The hard part will be the economic and finance theory.