The Role of AI in Economic Empirical Analysis: Focus on Large Language Models in Computational Economics

This article analyzes the integration of Large Language Models LLMs into economic empirical analysis, particularly in computational economics, exploring theoretical contributions, implementation challenges, and 1-5 year outlooks.​Theoretically, LLMs revolutionize economic modeling. They enable realistic agent-based simulations with diverse decision-making, automate economic relationship identification for improved causal inference, and integrate multi-modal data for better financial/economic forecasting. Additionally, LLMs capture behavioral nuances for personalized economic analysis.​ However, practical implementation is hindered by data limitations availability, quality, privacy, model opacity, generalization issues, alignment with economic theory, and interdisciplinary expertise gaps.​ In the near future, specialized economic LLMs, enhanced agent-based frameworks, advanced causal inference methods, and real-time economic monitoring tools are expected. These will reshape policy analysis and data interpretation, though ethical and regulatory adaptations are needed to address bias and transparency.​In conclusion, LLMs hold transformative potential for computational economics, contingent on overcoming technical and institutional hurdles while integrating theory and practice.