Zhigang Feng · Professor of Economics
E-mail: z.feng2@gmail.com
This course covers what modern AI changes about how economic research is actually done. It runs across six modules: foundations and what AI is; machine-learning essentials for macroeconomists; deep learning for solving dynamic models; reinforcement learning and heterogeneous-agent models; large language models and text as economic data, including retrieval-augmented generation; and agentic AI with case studies. The organizing premise is that as AI absorbs more of the implementation, the economist's edge shifts to designing algorithms and validating results.
Every lab is a Jupyter notebook that runs on CPU alone — in GitHub Codespaces or on your own machine — so nothing is gated behind special hardware. In practice the deep-learning and reinforcement-learning labs train noticeably faster on a GPU, so use one if you have access; none of them require it.
Prerequisites and where to start. The course assumes you are comfortable with dynamic macroeconomic models — if you would like that theory firmer first, a recorded offering of Advanced Macro Theory covers it in twenty-eight lectures, from the Solow model through recursive optimization, competitive equilibrium, and asset pricing. Python experience helps but is not required: the companion course, Quantitative Macroeconomics with AI and Machine Learning, teaches it from scratch in Topic 3, Programming Basics for Economists, and covers the classical computational methods — dynamic programming, perturbation, projection, parallel computing. The three are designed as one sequence: macro theory → classical methods → machine learning → agentic AI research, and this site is the last stage.
Where to enter. With no Python background, take the companion course's Topic 3 first, then start here at Module 1 with the setup lab. With classical computational training already in hand, Modules 3–4 (deep learning and RL for dynamic models) are the bridge you are looking for. Interested only in text and agents, Modules 5–6 stand on their own. When you want to build something, go to the project tracks.
| Date | Time | Location | Module |
|---|---|---|---|
| Tue, October 13 | Morning 9:30–12:30 | TBA · 待定 | Module 1 — Foundations (Lec 1–2) |
| Tue, October 13 | Afternoon 14:30–17:30 | TBA · 待定 | Module 2 — ML Essentials (Lec 3) |
| Thu, October 15 | Morning 9:30–12:30 | TBA · 待定 | Module 3 — Deep Learning for Macro (Lec 4) |
| Fri, October 16 | Morning 9:30–12:30 | TBA · 待定 | Module 4 — RL & Heterogeneous Agents (Lec 5–6) |
| Tue, October 20 | Morning 9:30–12:30 | TBA · 待定 | Module 5 — LLMs & Text (Lec 7–8) |
| Thu, October 22 | Morning 9:30–12:30 | TBA · 待定 | Module 6 — Agentic AI & Case Studies (Lec 9–10) |
| Module | Focus | Lectures | Length |
|---|---|---|---|
| 1 | Foundations — AI and economic research; what AI is | 1–2 | ≈4 hrs |
| 2 | Machine learning essentials for macroeconomists | 3 | ≈4 hrs |
| 3 | Deep learning for dynamic macro models | 4 | ≈4 hrs |
| 4 | Reinforcement learning & heterogeneous agents | 5–6 | ≈4 hrs |
| 5 | Language models & text as economic data | 7–8 | ≈4 hrs |
| 6 | Agentic AI & case studies | 9–10 | ≈4 hrs |
This is the last of three courses that run as one sequence. Advanced Macro Theory supplies the models — twenty-eight recorded lectures from the Solow model through recursive optimization, competitive equilibrium, asset pricing, and fiscal policy. Quantitative Macroeconomics with AI and Machine Learning solves them on a computer — twelve topics on value function iteration, time iteration, EGM, perturbation, projection, and high-performance computing, with slides, labs, and a complete video series (free) from 2026 and 2025, and it is the written subject of the textbook 机器学习与数量宏观经济学. This site takes them to the AI frontier.
Macro Theory → Classical Methods → ML & Deep Learning → Agentic AI Research
You can enter at any stage. This site is the last one.