Collaborative Research: Diagnostic Expectations, Uncertainty, and Macroeconomic Fluctuations

This award funds research in macroeconomics and bounded rationality. The team starts with the observation that in the formalization of representativeness (Kahneman and Tversky, 1972) developed by Gennaioli and Shleifer (2010), overreaction and confidence are affected by uncertainty, as a news effect interacts with an uncertainty effect. In the time series domain, this interaction emerges in a smooth version of Diagnostic Expectations (DE). Under Smooth Diagnostic Expectations (Smooth DE), agents overreact to new information. Since new information typically changes not just the conditional mean, but also the conditional uncertainty, changes in uncertainty surrounding current and past beliefs affect the severity of the DE distortion and confidence. As a result, Smooth DE ends up connecting two vastly popular branches of Economics that have largely proceeded in parallel: the Diagnostic Expectations literature and the Uncertainty literature (Bloom, 2014). The research consists of three projects. In the first project, the team highlights the inherent link between representativeness and uncertainty and introduces Smooth DE as the natural time series formalization of such a link. Under Smooth DE, agents over-react to new information as captured by the change in the current distribution of future events with respect to a reference distribution. Changes in uncertainty surrounding current and past beliefs affect the extent of the DE distortion. Smooth DE implies a joint and parsimonious micro-foundation for key properties of survey data: (1) overreaction of conditional mean to news, (2) stronger overreaction for weaker signals and longer forecast horizons, and (3) overconfidence in subjective uncertainty. In the second project, the team studies quantitative business cycle models that leverage insights from the Smooth DE framework, as well as from the team’s previous work on DE, imperfect information, and non-linear solution methods. The goal is to provide a rigorous and parsimonious account of business cycle properties that emerges from a smooth DE model with signal extraction. An analytical RBC model featuring Smooth DE accounts for overreaction and overconfidence in surveys, as well as three salient properties of the business cycle: (1) asymmetry, (2) countercyclical micro volatility, and (3) countercyclical macro volatility. A negative shock that raises perceived uncertainty increases the over-reaction to both idiosyncratic and aggregate shocks, and deepens the contraction. This rich and novel propagation arises because the intensity of the DE distortion is state-dependent. In the third project, the team focuses on actionable implications. Under smooth DE, the severity of the DE distortion varies in response to the level of uncertainty faced by agents. The team uncovers a novel role for decision makers: by reducing uncertainty, decision makers can now reduce the severity of the DE distortion and thus stabilize agents’ psychological biases. Thus, a redistributive rules that reduce cross-sectional uncertainty could also be beneficial for macroeconomic stabilization. The team studies the novel welfare implications of this belief stabilization This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.