Speaker:Dr. Zhigang Feng, Associate Professor (Department of Economics, University of Nebraska Omaha)
Host:Dr. Mingjia Xie, Assistant Professor (Li Anmin Institute of Economic Research, Liaoning University)
Guest Introduction:Dr. Rongsheng Tang, Associate Professor (Li Anmin Institute of Economic Research, Liaoning University)
Date and Time:December 10, 2024 (Tuesday), 10:00 AM - 11:30 AM (Beijing Time)
Venue:Wuzhou Yuan Conference Room, 1st Floor, Chongshan Campus, Liaoning University
Online Platform:Tencent Meeting 358-6417-4104
Language:Chinese/English
Abstract:
How can we account for the observed long swings in the U.S. national debt? Should fiscal policy be procyclical or countercyclical? We approach these questions within a rich quantitative framework for time-consistent fiscal policy. We first show that the steady-state debt dynamics are driven by the gap between the rate of time preference and the interest rate. Then, we will address two major fiscal transitions: a shift to a permanent low-interest rate scenario and the inevitability of an increase in mandatory spending. Moreover, we explore the dynamic responses of taxation and public spending to short-run productivity shocks and the political cycle. In our setting, fiscal policy is mostly procyclical.
Speaker Bio:
Dr. Zhigang Feng is an Associate Professor in the Department of Economics at the University of Nebraska Omaha. His research focuses on macroeconomics, artificial intelligence and machine learning, and computational economics. Some of his research has been funded by the Swiss National Science Foundation, the Swiss National Supercomputing Centre, and the U.S. National Science Foundation, and has been published in international journals such asInternational Economic Review,Quantitative Economics,Review of Economic Dynamics, andEconomic Theory. He also created the Bilibili channel “Zhongnan Macroeconomics,” where he teaches advanced macroeconomics, quantitative macroeconomic theory, and methods, and has authored the bookMachine Learning and Quantitative Macroeconomics: A Practical Guide with Pytorch.
