主題：Breaking the Curse of Dimensionality in Heterogeneous-Agent Models: A Deep Learning-Based Probabilistic Approach
報告人：黃吉，香港中文大學經濟學係助理教授
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 evalsuation 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.
報告人簡介：黃吉教授於2006年獲西南財經大學管理學學士, 2009年獲南開大學經濟學碩士, 2015年獲美國普林斯頓大學(Princeton University)經濟學博士.自2015年7月至2018年7月黃教授任職於新加坡國立大學經濟學係.黃教授的研究領域涉及影子銀行和宏觀金融,近期研究主要圍繞基於深度學習和概率論方法的高維連續時間模型求解,其關於影子銀行的學術論文發表於Journal of Economic Theory, Review of Finance.
時間：2024年1月8日（周一）中午12:00-1:30
地點：彩神v學院南路校區學術會堂712會議室
主辦單位：創新發展學院中國經濟與管理研究院
