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 and slides
The slide decks below follow the six-week lecture sequence. Each link opens the corresponding PDF.
Week 1 · Machine learning foundations
- Introduction to AI, machine learning, and economics — Slides (PDF)
- Model selection, shrinkage, and tree-based methods — Slides (PDF)
- Reading: Introduction to Statistical Learning, Chapters 1–4 and 8
Week 2 · Generative language modelling
- N-gram language models — Slides (PDF)
- Reading: Jurafsky and Martin, Chapter 3
Week 3 · Topic modelling
- Topic models and document classification — Slides (PDF)
- Reading: Blei, Ng, and Jordan, Latent Dirichlet Allocation
Week 4 · Embeddings and neural sequence models
- Word embeddings and classification — Slides (PDF)
- Neural networks and recurrent neural networks — Slides (PDF)
- Reading: Jurafsky and Martin, Chapter 5 and Chapter 7
Week 5 · Attention and transformers
- Attention and the transformer architecture — Slides (PDF)
- Reading: Jurafsky and Martin, Chapter 9
- Interactive visualisation: LLM visualisation
Week 6 · Working with LLMs and agentic AI
- Prompting, retrieval-augmented generation, local models, and agentic workflows — Slides (PDF)
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.
- Standalone executables: llama.cpp and built upon llamafile.cpp
- Serving own LLM with local API: Ollama with a nice setup instruction
- Most developed GUI model/platform wrapper: AnythingLLM
- Exciting projects: exolab inference framework (slicing LLM models)
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 covered
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.
Kase, H., Melosi, L., & Rottner, M. (2025). Estimating nonlinear heterogeneous agent models with neural networks (BIS Working Paper No. 1241). Bank for International Settlements. https://www.bis.org/publ/work1241.htm
Super technical but interesting
Adler, M., Alistarh, D., & Shavit, N. (2025). Towards combinatorial interpretability of neural computation (arXiv:2504.08842). arXiv. https://arxiv.org/abs/2504.08842
More accessible version: https://medium.com/@shanirgold/cracking-the-code-how-neural-networks-might-actually-think-ebac545e534e
Useful references
1. Introduction to AI in Economics
- Desai, A. (2023) – Machine Learning for Economics Research: When, What, and How?
- Bank of Canada Staff Analytical Note 2023-16
- https://www.bankofcanada.ca/2023/10/staff-analytical-note-2023-16/
- Cockburn, I. M., Henderson, R., & Stern, S. (2018) – The Impact of Artificial Intelligence on Innovation
- NBER Working Paper No. 24449
- https://www.nber.org/papers/w24449
- Athey, S. (2018) – The Impact of Machine Learning on Economics
- NBER Working Paper No. 24047
- https://www.nber.org/papers/w24047
2. Introduction of Machine Learning for Economists
- Hansen, S. (2020) – Machine Learning for Economics and Policy
- In: Artificial Intelligence: The Ambiguous Labor Market Impact in Developing Countries
- https://sekhansen.github.io/pdf_files/funcas_chapter.pdf
- Varian, H. R. (2014) – Big Data: New Tricks for Econometrics
- Journal of Economic Perspectives, 28(2), 3–28
- https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.28.2.3
- Mullainathan, S., & Spiess, J. (2017) – Machine Learning: An Applied Econometric Approach
- Journal of Economic Perspectives, 31(2), 87–106
- https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.87
3. Introduction to Natural Language Processing (NLP)
- Ash, E., Hansen, S., & McMahon, M. (2024) – Large Language Models in Economics
- Gentzkow, M., Kelly, B., & Taddy, M. (2019) – Text as Data
- Journal of Economic Literature, 57(3), 535–574
- https://www.aeaweb.org/articles?id=10.1257/jel.20181020
- Blei, D. M. (2012) – Probabilistic Topic Models
- Communications of the ACM, 55(4), 77–84
- https://dl.acm.org/doi/10.1145/2133806.2133826
- Mikolov, T., et al. (2013) – Efficient Estimation of Word Representations in Vector Space
- arXiv preprint
- https://arxiv.org/abs/1301.3781
4. Deep Learning in Economics
- Heaton, J. B., Polson, N. G., & Witte, J. H. (2017) – Deep Learning for Finance: Deep Portfolios
- Applied Stochastic Models in Business and Industry, 33(1), 3–12
- https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.2209
- Gu, S., Kelly, B., & Xiu, D. (2020) – Empirical Asset Pricing via Machine Learning
- Review of Financial Studies, 33(5), 2223–2273
- https://academic.oup.com/rfs/article/33/5/2223/5735583
- Korinek, A. (2023) – Generative AI for Economic Research: Use Cases and Implications for Economists
- Presented at NY Fed FinTech Conference
- https://www.newyorkfed.org/medialibrary/media/research/conference/2023/FinTech/400pm_Korinek_Paper_LLMs_final.pdf
5. AI for Data Collection and Cleaning in Economics
- Gentzkow, M., & Shapiro, J. M. (2011) – Ideological Segregation Online and Offline
- Quarterly Journal of Economics, 126(4), 1799–1839
- https://academic.oup.com/qje/article/126/4/1799/1900871
- Kum, H.-C., et al. (2014) – Privacy-Preserving Data Integration and Sharing
- Journal of Privacy and Confidentiality, 6(1)
- https://journalprivacyconfidentiality.org/index.php/jpc/article/view/675
- Cai, T., & Zhang, A. (2019) – Synthetic Data Generation for Statistical Disclosure Control via Conditional GANs
- arXiv preprint
- https://arxiv.org/abs/1909.11514
6. AI for Data Construction
- Lewis, P., et al. (2020) – Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- arXiv preprint
- https://arxiv.org/abs/2005.11401
7. Understanding AI: Privacy, Interpretability, and Integration with Econometrics
- Doshi-Velez, F., & Kim, B. (2017) – Towards A Rigorous Science of Interpretable Machine Learning
- arXiv preprint
- https://arxiv.org/abs/1702.08608
- Barocas, S., Hardt, M., & Narayanan, A. (2019) – Fairness and Machine Learning: Limitations and Opportunities
- Book (open access)
- https://fairmlbook.org/
- Athey, S., & Imbens, G. W. (2019) – Machine Learning Methods That Economists Should Know About
- Annual Review of Economics, 11, 685–725
- https://www.annualreviews.org/doi/10.1146/annurev-economics-080217-053433