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: Deep Learning for Solving Economic Models. 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 … 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.