Strategic Uncertainty over Business Cycles

  • 研討會日期 : 2021-02-05
  • 時間 : 10:30
  • 主講人 : Mr. Yu-Ting Chiang
  • 主持人 : Professor Terry Cheung
  • 地點 : online
  • 演講者簡介 : Mr. Chiang will receive his Ph.D. in Economics and Finance from University of Chicago in 2021. His research fields are Macroeconomics, Information Economics, and Macro-Finance. He is applying for a position of the Institute of Economics, Academia Sinica now.
  • 演講摘要 : This paper studies a dispersed information economy in which agents choose how much attention to pay to an unknown aggregate state. I show that under certain conditions, attention and four widely studied measures of uncertainty are countercyclical: agents pay attention when they expect the economy to be in a bad state, and this increase in attention alone leads to higher (i) conditional volatility of aggregate output, (ii) dispersion of individual output, (iii) forecast dispersion about aggregate output, and (iv) forecast uncertainty about aggregate output (i.e., forecast errors expected by each agent). As agents pay attention, they react more to the unknown state and their re-sponse generates high volatility. Because information is dispersed, agents’ beliefs and reactions diverge, and each of them faces higher uncertainty about others’ aggregate response. All these implications are consistent with data. I evaluate the mechanism quantitatively in a dynamic dispersed information economy calibrated to U.S. forecast survey data. Due to dispersed information, the economy features an “infinite regress problem” under which the equilibrium lacks a finite recursive state space. I solve the equilibrium attention and uncertainty dynamics using a new method developed in a companion paper. These dynamics are higher-order properties of the model that existing methods addressing the infinite regress problem cannot capture. In the calibrated economy, countercyclical attention generates fluctuations in all four measures of uncertainty with cyclicality, magnitude and persistence roughly consistent with data.