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

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

FEDERAL RESERVE BANK OF ST. LOUIS: How Combining Recursive and Rolling Forecasts Boosts Accuracy (Explained)

This episode dives into a Federal Reserve Bank of St. Louis research paper that examines how to make economic forecasts more accurate, particularly in an environment of structural change. We break down how combining "recursive" (using all available data) and "rolling" (using only recent data) forecasting methods can significantly improve prediction quality. Learn about this innovative strategy and share your feedback at feedback@econpod.org, or read the full paper at https://fedinprint.org/item/fedlwp/9611/original. This episode explains a real academic paper in plain English for a general audience. Source paper: FEDERAL RESERVE BANK OF ST. LOUIS Improving Forecast Accuracy by Combining Recursive and Rolling - FEDERAL RESERVE BANK OF ST. LOUIS https://doi.org/10.20955/wp.2008.028 Keywords: forecasting, macroeconomics, structural change, central banking, model averaging, forecast accuracy

Organisation for Economic Co-operation and Development: OECD: Predicting Recessions – Why 'Wisdom of Crowds' Rivals Machine Learning (Explained)

This episode delves into an OECD research paper that challenges conventional wisdom on forecasting recessions. We explore how a "wisdom of crowds" approach, averaging predictions from multiple simple models, can be as effective as advanced machine learning techniques like Random Forests for predicting economic downturns in OECD countries. Do you have thoughts on the best forecasting methods? Share them with us at feedback@econpod.org. This episode explains a real academic paper in plain English for a general audience. Source paper: Harnessing the wisdom - Organisation for Economic Co-operation and Development https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/harnessing-the-wisdom-of-crowds-to-assess-recession-risks-in-oecd-countries_d197200d/46880adc-en.pdf Keywords: Recession, Economic Forecasting, Macroeconomics, Machine Learning, Wisdom of Crowds, OECD