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
Presenter: Carolin Pflueger Affiliation: University of Chicago, Harris School of Public Policy Paper: Perceptions about Monetary Policy Date: 18th April, 2023 Time: 12:00 GMT (15:00 Israel Time) Abstract: We estimate perceptions about the Fed’s monetary policy rule from micro data on professional forecasters. The perceived rule varies significantly over time, with important consequences for monetary … Read more
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.