Hangjun He is a Computational Economist at the Penn Wharton Budget Model, where he develops and solves large-scale general equilibrium models of the U.S. economy for fiscal policy analysis. His work combines mathematical modeling, GPU-accelerated parallel computing, and machine learning to address computational challenges in macroeconomic and financial models.
His research spans public finance, macroeconomics, and computational economics, with contributions to both economic theory and computational methods. His work includes using neural networks and reinforcement learning to design efficient tax systems and examining how climate and entrepreneurial risks shape macroeconomic outcomes and asset prices, including the equity premium puzzle.
Hangjun received his Ph.D. and M.A. in Applied Mathematics and Computational Science from the University of Pennsylvania, where his dissertation was advised by Kent Smetters, and his B.S. in Mathematics and Applied Mathematics from Zhejiang University of Technology.
We estimate that the United States federal debt cannot rationally exceed roughly 210 percent of GDP as an outer limit. Under historical excess cost growth in healthcare, this outer limit is likely reached within 20 years; there is a 25% chance of reaching it in 14 years. Debt markets unravel earlier if beliefs about government repayment shift.
