Yu-Ting Chiang

I am an economist at the Federal Reserve Bank of St. Louis. I received my PhD from the Department of Economics and Booth School of Business at the University of Chicago in 2021.

My research studies how information frictions affect macroeconomic dynamics, especially their impact on macroeconomic uncertainty, volatility and cross-sectional outcomes.

You can contact me at yu-ting.chiang@stls.frb.org

Working papers

Attention and Fluctuations in Macroeconomic Uncertainty

Abstract: This paper studies a dispersed information economy in which agents can exert costly attention to learn about an unknown aggregate state of the economy. Under certain conditions, attention and four measures of uncertainty are countercyclical: Agents pay more attention when they expect the economy to be in a bad state, and their reaction generates higher (i) aggregate output volatility, (ii) cross-sectional output dispersion, (iii) forecast dispersion about aggregate output, and (iv) subjective uncertainty about aggregate output faced by each agent. All these phenomena are prominent features of the data. When attention cost is calibrated to U.S. forecast survey data, the model generates countercyclical fluctuations in attention and uncertainty, consistent with un- targeted moments from the data. A new method is developed to solve higher-order dynamics of the equilibrium under an infinite regress problem. This method is neces- sary to capture fluctuations in attention and uncertainty under dispersed information.

A Higher-Order Approximation Method for Dispersed Information Models

Abstract: This paper develops a higher-order approximation method for models with dispersed information. Dispersed information models provide promising explanations for important empirical regularities, but the literature has been constrained to analyzing models with first-order approximation methods. First-order approximations miss important features in these models. With first-order approximations, agents don’t respond to uncertainty in models featuring strategic uncertainty, the distribution of beliefs has no role beyond its average, and attention choices are static in business cycle models. I develop a perturbation-based method that overcomes these limitations. The method generalizes existing first order methods to arbitrarily higher-order approximations. For static dispersed information models, the method allows one to characterize higher-order properties of the equilibrium in closed form. For dynamic dispersed information models with an infinite regress problem, the method provides a simple algorithm for solving higher order dynamics of the equilibrium.

Media Competition for Attention

Abstract: This paper shows competition for attention between information providers, such as media, can lead to a decrease in the information available in an economy. I consider a model where information providers provide content (signals) to maximize how much attention they receive from agents; agents allocate attention optimally to acquire information about an unknown state. Information providers are concerned that, given limited attention, providing too much information in their content makes it hard for agents to ``understand''. If this concern is more severe when attention is scarce, I show an increase in the number of providers motivates each of them to provide less information in their content because agents allocate attention to content that is easy to understand. Moreover, if providers share a common information source and the number of providers is large, an increase in the number of providers leads to a decrease in the information available in the economy as measured by the precision of an average action taken by agents in response to the state.