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
IMPACT FACTOR 2018: 0.173
5-year IMPACT FACTOR: 0.248
CiteScore 2018: 0.24
SCImago Journal Rank (SJR) 2018: 0.163
Source Normalized Impact per Paper (SNIP) 2018: 0.186
Mathematical Citation Quotient (MCQ) 2018: 0.08
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
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