"Uncertainty and Disagreement in Business Cycles" (Working Paper)

Using a structural model, I analyze how changes in the distribution of signals about unknown economic conditions affect real aggregate macrovariables in the business cycle. I focus on two quantifiable properties of the distribution of signals, the signal accuracy and the correlation structure across signals, and analyze how time variation in these two properties affect an agent's decisions through his posterior beliefs. Since the exact signals agents use are difficult to observe empirically, I define two key concepts, uncertainty and disagreement, that capture dynamics in the distribution of signals and can be linked to data. Uncertainty is defined as the dispersion within each agent's forecasts about economic conditions. Disagreement is defined as the dispersion across agents in their mean forecasts about economic conditions. I show that uncertainty and disagreement affect an agent's controls through his first and higher order beliefs about economic conditions. Calibrating to US macrodata and the Survey of Professional Forecasters, I show empirically that the distribution of signals matters for aggregate dynamics and that my model mechanism can parsimoniously match the magnitude and sign of these effects. However, I find movements in the distribution of signals represent only a small fraction of the total variation in aggregate variables.

"Twisted Probabilities, Uncertainty, and Prices" with Lars P. Hansen, Balint Szöke, and Thomas J. Sargent

Published in Journal of Econometrics 216, 1 (2020)

A decision maker constructs a convex set of nonnegative martingales to use as likelihood ratios that represent alternatives that are statistically close to a decision maker's baseline model. The set is twisted to include some specific models of interest. Max-min expected utility over that set gives rise to equilibrium prices of model uncertainty expressed as worst-case distortions to drifts in a representative investor's baseline model. Three quantitative illustrations start with baseline models having exogenous long-run risks in technology shocks. These put endogenous long-run risks into consumption dynamics that differ in details that depend on how shocks affect returns to capital stocks. We describe sets of alternatives to a baseline model that generate countercyclical prices of uncertainty.

"Measuring the elastic modulus of microgels using microdrops" with Adam R. Abate, Lihua Jin, Zhigang Suo, and David A. Weitz

Published in Soft Matter 8, 10032 (2012)

Two microgel particles are encapsulated in a microdrop having a spherical diameter smaller than the sum of the diameters of the microgels; this causes the microgels to be squeezed together by the oil–water interface of the drop, in turn, making the drop ellipsoidal in shape. By modeling the force applied to the microgels by the drop and equating this to the Hertz contact force of their deformation, we are able to estimate their elastic modulus. By varying the surface tension and shape of the drops, we are able to measure the modulus of the microgels under different loads. This provides a simple technique for quantifying the elasticity of small, deformable objects, including liquid drops, microgels, and cells.