Machine Learning

18.11.2025: Jesús Fernández-Villaverde – Deep learning for solving economic models

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Presenter: Jesús Fernández-Villaverde Affiliation: University of Pennsylvania, Department of Economics. Paper – Slides Date: November 18, 2025. Time: 15:00 Israel Time. Abstract: The ongoing revolution in artificial intelligence, especially deep learning, is transforming research across many fields, including economics. Its impact is particularly strong in solving equilibrium economic models. These models often lack closed-form solutions, … Read more

05.03.2024: Jules van Binsbergen – (Almost) 200 years of news-based economic sentiment

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Presenter: Jules van Binsbergen Affiliation: University of Pennsylvania, Wharton School. Paper: (Almost) 200 Years of News-Based Economic Sentiment. Date: March 05, 2024. Time: 13:00 GMT (15:00 Israel Time) Abstract: Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country … Read more

29.06.2021: Sydney Ludvigson – Belief distortions and macroeconomic fluctuations

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This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (belief distortions) embedded in survey responses. We find that distortions are large on average even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to other information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with over-optimism associated with an increase in aggregate economic activity. Biases in expectations evolve dynamically in response to cyclical shocks. Biases about economic growth display greater initial under-reaction while those about inflation display greater delayed over-reaction.