Thiemo Fetzer | Professor of Economics

Machine Learning and AI in Economics

This module will be a broad brush covering basics in machine learning and information economics tracing the evolution focusing on applications of regression and simple machine learning techniques, towards language modelling, neural networks and the transformer based tools such as large language models. The course will be delivered mostly as a set of lectures drawing examples from research. Students will be asked to discuss (applied) research papers in the course. The list of topics that the course will touch upon at varying degrees of sophistication. The course will also provide a running commentary on recent technological and regulatory developments.

Required background:

  • Econometric training with traditional causal evaluation toolkit
  • Hands on research experience working with data, structured and unstructured
  • Understanding of computer architecture, basic computer science background
  • For practical applications laptop/machine with at least 8 GB of memory, ideally 16 GB for LLM deployment
  • Decent understanding in matrix algebra, real analysis,…

Lectures
Lecture material and slides are made available to the course participants as a shared folder.

  • Week 1
  • Week 2
  • Week 3
  • Week 4
    • Lecture 4: Friday 02.05.2025, 14:00 – 17:00 (3h, 1h hands on) Reinhard Selten Institute/RSI
  • Week 5
    • Lecture 5: Thursday 08.05.2025: 16:00 – 18:00 (2h) Hoersaal L Juridicum
      • Neural Networks and Recurrent Neural Networks
    • Lecture 6: Friday 09.05.2025: 14:00 – 17:00 (2h) Reinhard Selten Institute/RSI
      • Transformer architecture
  • Week 6:
    • Remote lecture
  • Week 7
    • Lecture 7: Thursday 22.05.2025: 16:00 – 18:00 (2h) Hoersaal L Juridicum
      • Techniques for working with LLMs
    • Lecture 8: Friday 23.05.2025: 14:00-17:00 (3h) Reinhard Selten Institute/RSI
      • Prompt engineering, retrieval augmented generation, and other AI workflows
  • Week 8:
    • No lecture – self work on readings
  • Week 9:
    • No lecture – Pentecost break
  • Week 10:
    • Lecture 9: 19.06.2025: Paper Presentations
    • Lecture 10: 20.06.2025: Paper Presentations

Self Sovereign AI

I am a big supporter of self-sovereign AI along with Open Source. The technology is incredibly useful and powerful but too powerful to be left for a few transnational companies or governments to control. This is why I want to highlight many of the use cases using local large language models. There are many ways of serving a large language model.

Topics to be covered

Introduction to AI in Economics

  • Overview of AI and machine learning
  • Historical perspective on AI in economic research
  • Potential applications and limitations

Introduction of Machine Learning for Economists

  • Supervised vs. unsupervised learning
  • Common algorithms: regression, classification, clustering
  • Model evaluation and validation techniques

Introduction to Natural Language Processing

  • Introduction to generative language modeling (N-gram language model,…)
  • Text analysis and sentiment analysis
  • Topic modeling and document classification
  • Embeddings

Deep Learning in Economics

  • Neural networks and their applications in economics
  • Convolutional and recurrent neural networks

Working with self-sovereign AI

  • Technical setup to work with transformer-based models on own devices (architectures such as Ollama, llama.cpp, llamafile, …)
  • Coding assistance and review

AI for Data Collection and Cleaning in Economics

  • Web scraping and automated data collection
  • AI-assisted data cleaning and imputation
  • Creating synthetic datasets for economic research

AI for Data Construction

  • Retrieval augmented generation
  • AI for data harmonization
  • Researcher and AI collaboration framework
  • Understanding AI

Privacy concerns and data protection

  • Emerging avenues for reconciling traditional econometric approaches with AI and Neural Network based structures
  • Interpretability and explainability of AI in economics

Papers assigned

Fetzer, Thiemo and Lambert, Peter and Feld, Bennet and Garg, Prashant, AI-Generated Production Networks: Measurement and Applications to Global Trade (November 17, 2024). CESifo Working Paper Series No. 11497, Available at SSRN: https://ssrn.com/abstract=5050959.

Beckmann, Lars and Beckmeyer, Heiner and Filippou, Ilias and Menze, Stefan and Zhou, Guofu, Unusual Financial Communication: ChatGPT, Earnings Calls, and Financial Markets (January 15, 2024). Olin Business School Center for Finance & Accounting Research Paper No. 2024/02, Available at SSRN: https://ssrn.com/abstract=4699231. 

Nian Li, Chen Gao, Mingyu Li, Yong Li, and Qingmin Liao. 2024. EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15523–15536, Bangkok, Thailand.

Li, Edward Xuejun and Tu, Zhiyuan and Zhou, Dexin, The Promise and Peril of Generative AI: Evidence from GPT-4 as Sell-Side Analysts(December 01, 2024). Available at SSRN: https://ssrn.com/abstract=4480947 

Garg, Prashant and Fetzer, Thiemo, Causal Claims in Economics (November 04, 2024). CESifo Working Paper Series No. 11462, Available at SSRN: https://ssrn.com/abstract=5045487.

Bartik, Alexander and Gupta, Arpit and Milo, Daniel, The Costs of Housing Regulation: Evidence From Generative Regulatory Measurement (March 20, 2025). Available at SSRN: https://ssrn.com/abstract=4627587.

Useful references

1. Introduction to AI in Economics

2. Introduction of Machine Learning for Economists

3. Introduction to Natural Language Processing (NLP)

4. Deep Learning in Economics

5. AI for Data Collection and Cleaning in Economics

6. AI for Data Construction

7. Understanding AI: Privacy, Interpretability, and Integration with Econometrics