{"id":5565,"date":"2025-10-01T15:20:24","date_gmt":"2025-10-01T15:20:24","guid":{"rendered":"https:\/\/tamver.eu\/?p=5565"},"modified":"2025-10-01T15:20:24","modified_gmt":"2025-10-01T15:20:24","slug":"zero-shot-models-in-economics-a-newfrontier","status":"publish","type":"post","link":"https:\/\/tamver.eu\/es\/zero-shot-models-in-economics-a-newfrontier\/","title":{"rendered":"Zero-Shot Models in Economics: A NewFrontier?"},"content":{"rendered":"\n

The Rise of Zero-Shot Modeling<\/h2>\n\n\n\n


Artificial intelligence is reshaping economics. A key innovation is the zero-shot model, AI, thatcan solve tasks it was never trained on. For economists, this means faster forecasts, new ways
to use data, and insights without custom-built models<\/p>\n\n\n\n

Why Zero-Shot Matters<\/h2>\n\n\n\n


Speed and Scale<\/strong>: Economists usually build new models for each case. Zero-shot systems
adapt instantly, saving time and effort.
Bridging Data Gaps<\/strong>: In regions with scarce data, zero-shot approaches provide useful insights.
Merging Text and Numbers<\/strong>: Zero-shot AI can read corporate transcripts, central bank
minutes, and combine them with data. This creates richer economic signals.<\/p>\n\n\n\n

According to McKinsey, generative AI could add $2.6\u2013$4.4 trillion annually to the global
economy. (McKinsey)<\/em><\/p>\n\n\n\n

Evidence From Research<\/h2>\n\n\n\n

Studies show that time-series foundation models (TSFMs) can forecast GDP in zero-shot mode.
They perform well in stable periods but weaken during shocks. (Arxiv)<\/p>\n\n\n\n

Figure 1. Performance of Zero-Shot Models vs. Classical Forecasting (2000\u20132024)<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Zero-shot learning also predicts the S&P 500 index from constituent data with strong accuracy.
And text-based models already classify financial disclosures effectively. (BIS, PLOS)<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Opportunities and Risks<\/h2>\n\n\n\n

Opportunities:<\/h2>\n\n\n\n