ECON408: Computational Methods in Macroeconomics, Winter 2024

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Syllabus

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

Problem Sets and Exams

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. Review February 26th - Midterm Practice Problems
6. February 28th - MIDTERM EXAM IN CLASS
7. Due Midnight PST on March 24th - Problem Set 4
8. Due Midnight PST on April 9th - Problem Set 5
9. Reviewing April 10th/12th - Final Practice Problems

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

Lectures

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 - Finish Wealth Distribution, Firm Dynamics, and Inequality and start Linear State Space Models
    • Lecture Notes: Wealth Distribution, Firm Dynamics, and Inequality and Linear State Space Models, Asset Pricing, and the Kalman Filter
    • References:
      • Wealth Distribution Dynamics
      • Linear State Space Models
12. February 14th - Linear State Space Models, Asset Pricing, and the Kalman Filter
    • Lecture Notes: Linear State Space Models, Asset Pricing, and the Kalman Filter
    • References:
      • Linear State Space Models
      • A First Look at the Kalman Filter
13. February 19th - SPRING BREAK
14. February 21st - SPRING BREAK
15. February 26th - Midterm Review + Practice Exam Logistics
16. February 28th - Midterm
17. March 4th - Last minute class cancellation
18. March 6th - Optimal Consumption and Savings Decisions and the Permanent Income Model
    • Lecture Notes: Optimal Consumption, Savings, and the Permanent Income Model
    • References:
      • Optimal Savings I: The Permanent Income Model
19. March 11th - Finish Stochastic Version of the Permanent Income Model and Start Markov Chains
    • Lecture Notes: Optimal Consumption, Savings, and the Permanent Income Model and Markov Chains with Applications to Unemployment and Asset Pricing"
    • References:
      • Optimal Savings I: The Permanent Income Model
      • Finite Markov Chains
      • A Lake Model of Employment and Unemployment
20. March 13th - Finish Markov Chains and Unemployment and Start Search
    • Lecture Notes: Markov Chains with Applications to Unemployment and Asset Pricing" and Search
    • References:
      • Finite Markov Chains
      • A Lake Model of Employment and Unemployment
21. March 18th - The McCall Search Model and Dynamic Programming
    • Lecture Notes: Search and Dynamic Programming
    • References:
      • Job Search I: The McCall Search Model
      • Job Search II: Search and Separation
      • A Lake Model of Employment and Unemployment
22. March 20th - Asset Pricing, and "Lucas Trees"
    • Lecture Notes: Asset Pricing, Lucas Trees, and Options
    • References:
      • Asset Pricing I: Finite State Models
      • Asset Pricing II: The Lucas Asset Pricing Model
23. March 25th - Problem Set and Midterm Exam Review
24. March 27th - Guest Lecture by Paul Beaudry
25. April 1st - STATUTORY HOLIDAY
26. April 3rd - More on Asset Pricing and Option Pricing
    • Lecture Notes: Asset Pricing, Lucas Trees, and Options
    • References:
      • Asset Pricing I: Finite State Models
      • Asset Pricing II: The Lucas Asset Pricing Model
27. April 8th - (Time Permitting) Rational Expectations Equilibrium with Firm Dynamics (and the big-K, little-k trick)
    • Lecture Notes: TBD
    • References:
      • Rational Expectations Equilibrium
28. April 10th - Finish Rational Expectations, Problem Set Review, and Final Questions

Software and Programming Environment

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.