FIE463: Numerical Methods in Macroeconomics and Finance using Python

Course material for spring term 2026 (V26) — Author: Richard Foltyn

Course outline

L = Lecture, W = Workshop

Part 1 — Introduction to Python

Week	Date	L/W	Topic	Notes & Exercises	Solutions
2	Tue, Jan 15	L	Introduction	Slides	—
3	Tue, Jan 20	L	Language & NumPy basics	Notebook, PDF	—
	Thu, Jan 22	W	Language & NumPy basics	Notebook, PDF	Notebook, PDF
4	Tue, Jan 27	L	Control flow & list comprehensions	Notebook, PDF	—
	Thu, Jan 29	W	Control flow & list comprehensions	Notebook, PDF	Notebook, PDF
5	Tue, Feb 3	L	Functions & modules	Notebook, PDF	—
	Thu, Feb 5	W	Functions & modules	Notebook, PDF	Notebook, PDF
6	Tue, Feb 10	L	Random numbers & plotting	Notebook, PDF	—
	Thu, Feb 12	W	Random numbers & plotting	Notebook, PDF	Notebook, PDF

Part 2 — Applications to models in macroeconomics & finance

Week	Date	L/W	Topic	Notes & Exercises	Solutions
7	Tue, Feb 17	L	Numerical optimization	Notebook, PDF	—
	Thu, Feb 19	W	Numerical optimization	Notebook, PDF	Notebook, PDF
8	Tue, Feb 24	L	General equilibrium	Notebook, PDF	—
	Thu, Feb 26	W	General equilibrium	Notebook, PDF	Notebook, PDF
9	Tue, Mar 3	L	Overlapping-generations models	Notebook, PDF	—
	Thu, Mar 5	W	VS Code & GitHub Copilot (applied to OLG)	Notebook, PDF	TBA
10	Tue, Mar 10	L	Stochastic processes & wealth dynamics	Notebook, PDF	—
	Thu, Mar 12	W	Stochastic processes & wealth dynamics	Notebook, PDF	TBA

Part 3 — Working with financial data

Week	Date	L/W	Topic	Notes & Exercises	Solutions
11	Tue, Mar 17	L	Data processing with Pandas	Notebook, PDF	—
	Thu, Mar 19	W	Data processing with Pandas	Notebook, PDF	TBA
12	Tue, Mar 24	L	Intro to scikit-learn	Notebook, PDF	—
	Thu, Mar 26	W	Intro to scikit-learn	Notebook, PDF	TBA
13	Tue, Mar 31	L	Regression models	Notebook, PDF	—
	Thu, Apr 2	W	No workshop (Easter break)	—	—
14	Tue, Apr 7	L	Classification models	Notebook, PDF	—
	Thu, Apr 9	W	Regression & classification models	Notebook, PDF	TBA

Guides

See the guides/ folder for instructions on how to install Anaconda (Python), Visual Studio Code, and git version control.

Forking & Cloning

Forking

• Click on the Fork icon to fork this repository (create your own personal copy)
• In the future, you need to click on Sync Fork to get new commits made to this repository into your forked version.

Cloning

1. Click on the green Code icon to clone the repository to your computer

2. Select HTTPS or SSH depending on your authentication method (SSH keys will only work if you have configured them) and copy the URL.

3. You can clone the repository directly in Visual Studio Code, or use the command line:

   Using HTTPS (no SSH key configured):

   git clone https://github.com/richardfoltyn/FIE463-V26.git

   Using SSH keys:

   git clone git@github.com:richardfoltyn/FIE463-V26.git

Creating a Conda environment

Using the Anaconda Prompt (Windows) or Terminal (macOS), you can use the environment definition file (environment.yml) provided in this repository to create a conda environment called FIE463:

conda env create -f environment.yml

Note that you first need to change to the directory where environment.yml is located for this to work.

If you don't know how to locate the environment.yml file on your system, you can also download it directly from GitHub and create the environment in one step:

curl -O https://raw.githubusercontent.com/richardfoltyn/FIE463-V26/main/environment.yml
conda env create -f environment.yml

Additional resources

1. Think Python by Allen B. Downey: general intro to Python, chapters are available as Jupyter notebooks.
2. Python for Everybody by Charles R. Severance: general intro to Python with a focus on data analysis, available as PDF.
3. QuantEcon: Python programming for economics & finance (beginners & advanced)
4. Introduction to Programming and Numerical Analysis: Python for macroeconomics, taught at the University of Copenhagen

License

This material is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except for the data files contained in the data/ folder, which fall under the terms imposed by the original content creators.