AI for Macroeconomic Analysis
Methods and Applications for Policy, Forecasting, and Risk Analysis
June 08-12 2026, Collegio Carlo Alberto, Turin, Italy.
June 08-12 2026, Collegio Carlo Alberto, Turin, Italy.
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This one-week event explores recent advances in the use of artificial intelligence (AI) for macroeconomic analysis. The program consists of a summer school followed by a research workshop.
Summer School
The course begins with an overview of the main AI and machine learning techniques that are increasingly used in economic analysis. It will then examine a series of empirical applications relevant for macroeconomic analysis and policy.
In the first part, the course introduces tree-based methods and neural networks, with particular emphasis on deep learning. It also discusses how these techniques can handle data types beyond traditional numerical inputs, including text and other unstructured sources. For neural networks, the course will present different architectures and build intuition about the types of problems for which each approach is most suitable.
The second part of the course provides an in-depth review—supported by practical examples—of selected applications of AI in macroeconomic analysis. These include methods for capturing non-linearities in economic relationships, conducting risk analysis, and incorporating non-traditional data into forecasting models.
Overall, the course aims to equip participants with a solid understanding of modern AI methods and the practical tools needed to develop their own applications for macroeconomic analysis.
Workshop
Confirmed participants: Francesco Bianchi (JHU), Christian Brownless (LUISS), Stephen Hansen (University College London), Klodiana Istrevi (ECB), Adrian Peralta (IMF), Galina Potjagailo (Bank of England), Leif Anders Thorsrud (BI Oslo).
The audience includes graduate students, academics, practitioners, and policymakers. Apply here.
Instructors
Instructors
Michele Lenza is Head of the Monetary Policy Research Division of the European Central Bank. He received a Ph.D. in Economics and Statistics from the Université Libre de Bruxelles and he is a Fellow of the Centre for Economic Policy Research (CEPR), the International Association for Applied Econometrics (IAAE) and the Euro Area Business Cycle Network. His research interests cover monetary economics, business cycle analysis and macroeconometric methods. His reserch appeared in peer-reviewed journals, such as Econometrica, the Journal of the American Statistical Association, the Journal of Econometrics, The Review of Economics and Statistics, the Journal of the European Economic Association and the Economic Journal, among others.
Gianluca Bontempi is Full Professor in the Department of Computer Science at the Université libre de Bruxelles (ULB), Belgium, and Founder and co-Head of the ULB Machine Learning Group (MLG). He is a leading researcher in machine learning, big data mining, predictive modelling and causal inference, with applications spanning time series forecasting, credit card fraud detection, bioinformatics and complex data-driven systems. Prof. Bontempi has authored hundreds of scientific publications, contributed to open-source software for data science, and serves in editorial and scientific advisory roles in international research communities. He is also active in initiatives such as TRAIL, FARI, WEL-RI and ELLIS, reflecting his commitment to interdisciplinary research and innovation in intelligent systems.
Monday
Monday
Lectures
From Linear Models to Tree‑Based Models
From Shallow to Deep Learning
From Linear Models to Tree‑Based Models
From Shallow to Deep Learning
Participant Presentations.
Tuesday
Tuesday
Lectures
Beyond Structured Numerical Data: Learning from Text
Beyond Structured Numerical Data: Learning from Text
Quantile regression forests for the analysis of the risks
Participant Presentations.
Wednesday
Wednesday
Lectures
Advanced topics with tree-based methods
Advanced topics with tree-based methods
Applications of neural networks
Participant Presentations.
Thursday
Thursday
Workshop, Day 1
Friday
Friday
Workshop, Day 2
Policy Panel: AI Tools for Monitoring the Macroeconomy and Financial Markets
Thursday, 11 June
How to participate?
How to participate?
Applications should be submitted before 15/5/2026, early applications are strongly encouraged.
Applications must include a CV and a short motivation statement (in the email body). Applicants wishing to present a paper (optional) should indicate this and attach a draft paper.
Acceptance decisions will be emailed by 20/5/2026 and may be communicated earlier for applications submitted before the deadline, in order to facilitate logistical arrangements.
Fees: €450 (PhD students), €900 (post-docs & faculty), €1350 (others), including VAT.
A limited number of places are available for online participants at a 15% discounted rate.
Fees cover registration, lunches, and coffee breaks.
All accepted participants must be SoFiE members (or join before confirmation). Membership info: http://sofie.stern.nyu.edu/membership
Venue
Venue
Collegio Carlo Alberto, Turin, Italy
Organizing institutions
Organizing institutions
Collegio Carlo Alberto, Turin
Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Turin
European Stability Mechanism
European Central Bank
Society for Financial Econometrics
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