chirindaopensource/auditable_AI_agent_loop_for_empirical_economics
End-to-End Python implementation of Shin (2026)'s evaluator-locked agentic loop for transparent empirical research. Combines LLM-driven specification search with immutable evaluation harnesses, penalized regression (peLASSO), and Diebold-Mariano testing on ECB forecast data. Addresses the "garden of forking paths" crisis in AI-driven economics.
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MIT
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Mar 22, 2026
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