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Biased Bayesian learning with an application to the risk-free rate puzzle

Listed author(s):
  • Ludwig, Alexander
  • Zimper, Alexander

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%. This is 1–2.5% closer to the empirical risk-free rate than according calibrations of the rational expectations model.

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Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 39 (2014)
Issue (Month): C ()
Pages: 79-97

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Handle: RePEc:eee:dyncon:v:39:y:2014:i:c:p:79-97
DOI: 10.1016/j.jedc.2013.11.007
Contact details of provider: Web page: http://www.elsevier.com/locate/jedc

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