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Non-Bayesian Learning

Larry Epstein (), Jawwad Noor () and Sandroni Alvaro ()
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Sandroni Alvaro: University of Pennsylvania

The B.E. Journal of Theoretical Economics, 2010, vol. 10, issue 1, 1-20

Abstract: A series of experiments suggest that, compared to the Bayesian benchmark, people may either underreact or overreact to new information. We consider a setting where agents repeatedly process new data. Our main result shows a basic distinction between the long-run beliefs of agents who underreact to information and agents who overreact to information. Like Bayesian learners, non-Bayesian updaters who underreact to observations eventually forecast accurately. Hence, underreaction may be a transient phenomenon. Non-Bayesian updaters who overreact to observations eventually forecast accurately with positive probability but may also, with positive probability, converge to incorrect forecasts. Hence, overreaction may have long-run consequences.

Date: 2010
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