Biased Bayesian learning with an application to the risk-free rate puzzle
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Date
Authors
Ludwig, Alexander
Zimper, Alexander
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
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 35 −5%. This is 1 −25%
closer to the empirical risk-free rate than according calibrations of the rational
expectations model.
Description
Keywords
Ambiguity, Non-additive probability measures, Bayesian learning, Truncated normal distribution, Risk-free rate puzzle
Sustainable Development Goals
Citation
Ludwig, A & Zimper, A 2014, 'Biased Bayesian learning with an application to the risk-free rate puzzle', Journal of Economic Dynamics and Control, vol. 39, pp. 79-97.