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    Bayesian learning with catastrophe risk : information externalities in a large economy

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    ZANTEDESCHI-THESIS.pdf (245.2Kb)
    Date
    2011-08
    Author
    Zantedeschi, Daniel
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    Abstract
    Based on a previous study by Amador and Weill (2009), I study the diffusion of dispersed private information in a large economy subject to a ”catastrophe risk” state. I assume that agents learn from the actions of oth- ers through two channels: a public channel, that represents learning from prices, and a bi-dimensional private channel that represents learning from lo- cal interactions via information concerning the good state and the catastrophe probability. I show an equilibrium solution based on conditional Bayes rule, which weakens the usual condition of ”slow learning” as presented in Amador and Weill and first introduced by Vives (1993). I study asymptotic conver- gence ”to the truth” deriving that ”catastrophe risk” can lead to ”non-linear” adjustments that could in principle explain fluctuations of price aggregates. I finally discuss robustness issues and potential applications of this work to models of ”reaching consensus”, ”investments under uncertainty”, ”market efficiency” and ”prediction markets”.
    Department
    Economics
    Description
    text
    Subject
    Bayesian learning
    Catastrophe risk
    Information externality
    URI
    http://hdl.handle.net/2152/ETD-UT-2011-08-3868
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