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The B.E. Journal of Theoretical Economics

Editor-in-Chief: Schipper, Burkhard

Ed. by Fong, Yuk-fai / Peeters, Ronald / Puzzello , Daniela / Rivas, Javier / Wenzelburger, Jan


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1935-1704
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Non-Bayesian Learning

Larry G Epstein / Jawwad Noor / Alvaro Sandroni
Published Online: 2010-01-29 | DOI: https://doi.org/10.2202/1935-1704.1623

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.

Keywords: non-Bayesian learning

About the article

Published Online: 2010-01-29


Citation Information: The B.E. Journal of Theoretical Economics, Volume 10, Issue 1, ISSN (Online) 1935-1704, DOI: https://doi.org/10.2202/1935-1704.1623.

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