主题：Breaking the Curse of Dimensionality in Heterogeneous-Agent Models: A Deep Learning-Based Probabilistic Approach
报告人：黄吉，香港中文大学经济学系助理教授
摘要：Dynamic heterogeneous-agent models share two features: 1) high-dimensional aggregate states that are beyond the control of individual agents, and 2) low-dimensional aggregate shocks. This paper exploits these two features using a deep learning-based probabilistic approach and demonstrates that it is possible to solve for the global solution of these models without compromising dimensionality reduction. The computational advantage lies in converting a conditional expectation equation into multiple equations of shock realizations, significantly enhancing evaluation efficiency. As an illustration, I solve the continuous-time version of Krusell and Smith (1997) with a two-asset portfolio choice and nonlinear debt market clearing condition.
报告人简介：黄吉教授于2006年获西南财经大学管理学学士, 2009年获南开大学经济学硕士, 2015年获美国普林斯顿大学(Princeton University)经济学博士.自2015年7月至2018年7月黄教授任职于新加坡国立大学经济学系.黄教授的研究领域涉及影子银行和宏观金融,近期研究主要围绕基于深度学习和概率论方法的高维连续时间模型求解,其关于影子银行的学术论文发表于Journal of Economic Theory, Review of Finance.
时间：2024年1月8日（周一）中午12:00-1:30
地点：中央财经大学学院南路校区学术会堂712会议室
主办单位：创新发展学院中国经济与管理研究院
