Directed Attention and Nonparametric Learning
We study an ambiguity-averse agent with uncertainty about income dynamics who chooses what aspects of the income process to learn about. The agent chooses to learn most about income dynamics at the very lowest frequencies, which have the greatest effect on utility. Deviations of consumption from the full-information benchmark are then largest at high frequencies, so consumption responds strongly to predictable changes in income in the short-run but is closer to a random walk in the long-run. Whereas ambiguity aversion typically leads agents to act as though shocks are more persistent than the truth, endogenous learning here eliminates that effect.
We appreciate helpful comments from Ben Hebert, Peter Klibanoff, Konstantin Milbradt, Mikkel Plagborg-Müller, and seminar participants. Nathanson thanks the Guthrie Center for Real Estate Research for financial support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Ian Dew-Becker & Charles G. Nathanson, 2019. "Directed attention and nonparametric learning," Journal of Economic Theory, . citation courtesy of