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Biased Bayesian Learning with an Application to the Risk-Free Rate Puzzle

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  • Alexander Ludwig
  • Alexander Zimper

Abstract

Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias that reflects the agent's ambiguity attitudes. By calibrating the standard equilibrium conditions of the consumption based asset pricing model we illustrate that our approach contributes towards a resolution of the risk-free rate puzzle. For a plausible parameterization we obtain a risk-free rate in the range of 3.5-5 percent. This is 1-2.5 percent closer to the empirical risk-free rate than according calibrations of the rational expectations model.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Alexander Ludwig & Alexander Zimper, 2013. "Biased Bayesian Learning with an Application to the Risk-Free Rate Puzzle," ERSA Working Paper Series 390, Economic Research Southern Africa.
    • Handle: RePEc:rza:ersawp:390
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    JEL classification:

    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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