Price probabilities: A class of Bayesian and non-Bayesian prediction rules

Filippo Massari

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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This paper examines the implications of the market selection hypothesis on the accuracy of the probabilities implied by equilibrium prices and on the “learning” mechanism of markets. I use the standard machinery of dynamic general equilibrium models to generate a rich class of probabilities, price probabilities, and discuss their properties. This class includes the Bayes’ rule and known non-Bayesian rules. If the prior support is well-specified, I prove that all members of this class perform as well as Bayes’ rule in terms of likelihood. If the prior support is misspecified in that the Bayesian prior does not converge, I demonstrate that some members of price probabilities significantly outperform Bayes’. Because these members are never worse and sometimes better than Bayes, my result challenges the prevailing opinion that Bayes’ rule is the only rational way to learn.

Original languageEnglish
Pages (from-to)133–166
Number of pages34
JournalEconomic Theory
Early online date20 May 2020
Publication statusPublished - Jul 2021
Externally publishedYes


  • Efficient market
  • Market selection hypothesis
  • Non-Bayesian learning
  • Prediction market

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