Huge, fancy AI models are actually worse at predicting the economy than the basic tools we were using ten years ago.
March 20, 2026
Original Paper
Machine Learning vs Foundation Models for Economic Forecasting Evidence from Blue Chip Indicators
SSRN · 6432986
The Takeaway
Despite being trained on billions of data points, 'parameter-heavy' neural networks fail on economic data because they lack the specific human-designed features that account for how indicators like unemployment actually move over time.
From the abstract
Foundation models pre-trained on billions of time series promise universal forecasting capabilities, yet their performance on low-frequency economic data with limited observations remains untested. <div> We evaluate ten forecasting models across four families (machine learning, deep learning, extended LSTM, and foundation models) on nine U.S. economic indicators using 49 years of Blue Chip consensus forecasts (1976-2025). We employ walk-forward validation with expanding windows and Diebold-Maria