Directed Attention and Nonparametric Learning / Ian Dew-Becker, Charles G. Nathanson.

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Dew-Becker, Ian

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TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w23917.Publication details: Cambridge, Mass. National Bureau of Economic Research 2017.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: 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.

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October 2017.

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.