Speaker：Dr. Shenghao Zhu

Title：Optimal Taxation with Incomplete Markets–An Exploration via Reinforcement Learning

Schedule: Oct 19, TH 1:30-2:30 PM

Location: 诚明楼 (Chengming Hall）RM 315

 

Introduction：

朱胜豪，对外经济贸易大学国际经济贸易学院教授、博士生导师，纽约大学经济学博士，曾任职新加坡国立大学经济系。朱胜豪教授的主要研究领域为：收入分配、金融经济学和宏观经济学。他的研究结合动态宏观经济学异质性模型和家庭微观大数据，利用计算技术解决微观主体决策的激励相容问题，通过数值模拟来设计最优经济政策。

朱胜豪教授曾在 Econometrica, Journal of Economic Theory, International Economic Review 等国际顶级期刊发表论文数篇，学术成果在Google Scholar上被国际同行引用800多次，并主持一项财税政策对收入分配和财富不平等影响的国家自然科学基金面上项目。

 

Abstract：In this paper we study the optimal taxation and government debt/asset management and price volatility in economy with incomplete markets. We develop a machine learning-based computational method to solve the equilibrium set of endogenous state variables, along with policy and value functions. With quasi-linear preference, any finite government asset is sustainable and the Ramsey government would play a Ponzi game in absence of restriction on taxation. When taxes or assets are bounded, the asset accumulation process becomes ergodic even when the government expenditure follows a Markov process. Furthermore, we show that the celebrated Martingale convergence result in the literature is due to the weak precautionary savings motive by the government. Once the government has sufficiently strong precautionary savings motive, the ergodic property of asset accumulation process will return. Our findings indicate that the optimal taxation and government debt/asset management are intrinsically correlated with significant implications on asset pricing.