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Non-Bayesian Learning

Author

Listed:
  • Epstein Larry G

    (Boston University, lepstein@bu.edu)

  • Noor Jawwad

    (Boston University, jnoor@bu.edu)

  • Sandroni Alvaro

    (University of Pennsylvania, sandroni@kellogg.northwestern.edu)

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.

Suggested Citation

  • Epstein Larry G & Noor Jawwad & Sandroni Alvaro, 2010. "Non-Bayesian Learning," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-20, January.
    • Handle: RePEc:bpj:bejtec:v:10:y:2010:i:1:n:3
    DOI: 10.2202/1935-1704.1623
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    References listed on IDEAS

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