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

Alexander Ludwig and Alexander Zimper

Journal of Economic Dynamics and Control, 2014, vol. 39, issue C, 79-97

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

Keywords: Ambiguity; Non-additive probability measures; Bayesian learning; Truncated normal distribution; Risk-free rate puzzle (search for similar items in EconPapers)
JEL-codes: D83 E44 G12 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Working Paper: Biased Bayesian learning with an application to the risk-free rate puzzle (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:39:y:2014:i:c:p:79-97

DOI: 10.1016/j.jedc.2013.11.007

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