This repo contains code for the tutorials of the Spring 2023 heterogeneous-agent macro workshop at the NBER taught by Adrien Auclert, Bence Bardóczy, Matt Rognlie, and Ludwig Straub.
An agenda for the workshop is available here:
https://www.nber.org/conferences/heterogeneous-agent-macro-workshop-spring-2023
This is also where we will post lecture notes and other reading materials. If you're familiar with git, you can clone this repository and pull the latest updates to the materials. Otherwise, you can download the latest version of the repository as a zip by clicking this link.
If you are relatively new to Python, we recommend having the Anaconda distribution of Python installed to make sure you have all necessary libraries.
There are many outstanding resources you can find online, but two good introductory resources are the introductory lecture series at QuantEcon and the Python data science handbook (ignoring the machine learning content in the latter).
If you are accustomed to Matlab or Julia, QuantEcon's Matlab-Python-Julia cheatsheet can be useful, as can NumPy for Matlab users (ignoring now-obsolete "matrix" class at the end).
Before we start on Monday, please watch the video recording of the first lecture, "Lecture 1: Standard Incomplete Markets Steady State".
The slides for the lecture are a Jupyter notebook, which you can view on GitHub here, or open on your own computer by navigating to Lectures/Lecture 1, Standard Incomplete Markets Steady State.ipynb in the repository. You are encouraged to follow along and play around with the notebook yourself!
Part 0: introduction (11:33 min)
Part 1: discretizating assets and income (21:00 min)
Part 2: backward iteration and policy functions (40:18 min)
Part 3: forward iteration and distribution (21:16 min)
Part 4: aggregation, applications, and expectation functions (41:02 min)