In August 2024, the key project "Machine Learning Solution Algorithms and Applied Research for Heterogeneous Dynamic Macroeconomic Models", submitted by Professor Nie Jun of our institute, was approved by the National Natural Science Foundation of China (NSFC) for the 2024 funding cycle, with a direct grant of 1.6 million RMB. Key participants from our institute include Associate Professor Liu Xiying, Associate Professor Liu Yan, and Associate Researcher Xiong Chen. External collaborators include Professor Zhao Yaohui, Professor Zhu Shenghao, Professor Chen Nan, Professor Cai Hengjin, Associate Professor Feng Zhigang, and Assistant Professor Yang Yucheng.

The National Natural Science Foundation serves as the primary channel for national funding of basic and applied research in China. The number of approved NSFC projects is a critical indicator of a university’s research capability and academic standing. The Foundation plays an indispensable role in advancing scientific innovation, cultivating research talent, fostering interdisciplinary collaboration, addressing national strategic needs, and enhancing global competitiveness.

Principal Investigator Profile: NIE Jun

• Position: Second-Class Professor, Doctoral Supervisor, Taikang Chair Professor, Dean of the School of Economics and Management, Wuhan University.

• Education: Dual B.A. in Economics and Mathematics (Wuhan University), Ph.D. in Economics (New York University).

• Advisor: Nobel Laureate Thomas Sargent, a leading scholar in macroeconomics.

• Research Focus: Macroeconomics and policy analysis, with over 50 published papers and research reports. His work has been widely cited in international media, and he has frequently been consulted by key policy institutions.

• Publications: Papers in top-tier journals such as Economic Journal, Journal of Economic Theory, Journal of International Economics, Journal of the European Economic Association, European Economic Review, Review of Economic Dynamics, and Journal of Economic Dynamics and Control.

• Editorial Role: Co-Editor-in-Chief of Annals of Economics and Finance (AEF), the first economics and finance journal in China indexed by SSCI.

This project represents a groundbreaking integration of machine learning and macroeconomics, with far-reaching implications for both academic research and real-world policy formulation. By bridging theoretical innovation and practical applications, it aims to advance macroeconomic modeling while addressing pressing challenges in China’s economic transition.

Research Objectives:

1. Developing Machine Learning-Based Algorithmic Frameworks for High-Dimensional Heterogeneous Dynamic Macro Models

• Challenge: Traditional methods struggle with computational inefficiency and low precision when handling dynamic problems involving endogenous distributions of heterogeneous agents.

• Solution: This project leverages machine learning (especially deep learning) to achieve global solutions for high-dimensional state variables, overcoming the "curse of dimensionality" and establishing a robust algorithmic foundation for broader applications of heterogeneous macroeconomic models.

2. Constructing China-Specific Heterogeneous Dynamic Macro Models for Policy Analysis

• Approach: Integrate Chinese micro-level big data with macroeconomic modeling to reflect unique features of China’s economy, such as:

  - Heterogeneity among households and firms.

  - Structural transformation and regional disparities.

• Innovation: Machine learning techniques will address challenges in high-dimensional data processing and model complexity, enabling dynamic equilibrium analysis of heterogeneous agents and quantitative policy evaluation.