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chirindaopensource / auditable_AI_agent_loop_for_empirical_economics

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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.

time-series reproducible-research scikit-learn cross-validation lasso econometrics ridge-regression audit-trail forecast-combination gdp-forecasting prompt-engineering diebold-mariano research-automation anthropic-claude llm-agents agentic-ai ecb-spf specification-search python-econometrics ast-sandboxing
  • Updated Mar 22, 2026
  • Jupyter Notebook

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