AI Economics Research Podcast

Federal Reserve Bank of New York Staff Reports: Federal Reserve Bank of New York: Decoding Demand Shocks in Retail & Services with Foot Traffic Data (Explained)

This episode dives into a Federal Reserve Bank of New York Staff Report that uses big data from foot traffic to uncover hidden demand shocks affecting businesses in New York City. We explore how retail, service, and health establishments experience highly varied and unpredictable changes in customer demand, challenging traditional economic assumptions. Hear the plain English explanation of this cutting-edge research and share your thoughts at feedback@econpod.org. This episode explains a real academic paper in plain English for a general audience. Source paper: NO. 1191 Shocks from Foot Traffic Marina Azzimonti | David Wiczer | Yang Xuan - Federal Reserve Bank of New York Staff Reports https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr1191.pdf?sc_lang=en Keywords: Foot Traffic, Demand Shocks, Retail Economics, Service Sector, Big Data, Economic Forecasting

Bank of England: Do AI Models Share Our Inflation Attitudes? (Explained)

This episode dives into a new Bank of England research paper exploring whether Large Language Models like GPT-3.5 Turbo can form inflation perceptions and expectations similar to human households. Using a clever quasi-experimental design, the study compares LLM outputs to survey data, revealing how AI responds to economic signals and its surprising sensitivity to food inflation. Discover the implications for economic forecasting and social science research, and share your thoughts at feedback@econpod.org. Find the full paper here: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2026/inflation-attitudes-of-large-language-models.pdf This episode explains a real academic paper in plain English for a general audience. Source paper: Inflation attitudes of large Nikoleta Anesti, Edward Hill and Andreas Joseph - Bank of England Keywords: inflation, large language models, AI, economic forecasting, central banking, macroeconomics

FEDERAL RESERVE BANK OF ST. LOUIS: The Economics of Narcoterrorism – Funding Terror Through Drugs and Countering It (Explained)

This episode breaks down a recent Federal Reserve Bank of St. Louis research paper, offering a strategic economic analysis of narcoterrorism in plain English. We explore how terrorist groups extort drug farmers for funding, how developed nations use crop destruction as a counterterrorism tool, and the complex interplay of drug markets, terror financing, and international policy. For questions or discussion, email feedback@econpod.org. This episode explains a real academic paper in plain English for a general audience. Source paper: FEDERAL RESERVE BANK OF ST. LOUIS A Strategic Analysis of Narcoterrorism: Counterterrorism, Terrorist - FEDERAL RESERVE BANK OF ST. LOUIS https://s3.amazonaws.com/real.stlouisfed.org/wp/2025/2025-032.pdf Keywords: Narcoterrorism, Counterterrorism, Drug Trafficking, Terrorist Financing, International Economics, Security Policy

FEDERAL RESERVE BANK OF ST. LOUIS: Does the Money Supply Predict Inflation in the US? (Explained)

This episode breaks down a research paper from the Federal Reserve Bank of St. Louis, exploring whether different measures of the money supply are useful for forecasting US inflation. Using advanced non-linear techniques, the authors find limited support for monetary aggregates as reliable inflation predictors in the early to mid-2000s. Dive into the specifics of this intriguing macroeconomic study at https://fedinprint.org/item/fedlwp/10440/original and share your thoughts at feedback@econpod.org. This episode explains a real academic paper in plain English for a general audience. Source paper: FEDERAL RESERVE BANK OF ST. LOUIS Does Money Matter in Inflation Forecasting? - FEDERAL RESERVE BANK OF ST. LOUIS https://doi.org/10.20955/wp.2009.030 Keywords: inflation, money supply, forecasting, monetary policy, central banking, macroeconomics

FEDERAL RESERVE BANK OF ST. LOUIS: Does Money Supply Really Predict Inflation? (Explained)

This episode dives into a Federal Reserve Bank of St. Louis research paper asking a crucial question: Do monetary aggregates actually help forecast inflation? We break down the paper's novel approach using neural networks and kernel regression to evaluate money's predictive power for US inflation in the early 2000s, explaining the findings in plain English. For more details, find the original paper at https://fedinprint.org/item/fedlwp/10440/original, and we welcome your feedback and discussion at feedback@econpod.org. This episode explains a real academic paper in plain English for a general audience. Source paper: FEDERAL RESERVE BANK OF ST. LOUIS Does Money Matter in Inflation Forecasting? - FEDERAL RESERVE BANK OF ST. LOUIS https://doi.org/10.20955/wp.2009.030 Keywords: Inflation, Money Supply, Forecasting, Macroeconomics, Central Banking, Neural Networks