Learning from ambiguous and misspecified models

Massimo Marinacci and Filippo Massari

Journal of Mathematical Economics, 2019, vol. 84, issue C, 144-149

Abstract: We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one prior distribution over a set of models and provide sufficient conditions for ambiguity to fade away because of learning. Our conditions apply to most learning environments: iid and non-iid model-classes, well-specified and misspecified model-classes/prior support pairs. We show that ambiguity fades away if the empirical evidence supports a set of models with identical predictions, a condition much weaker than learning the truth.

Keywords: Ambiguity; Learning; Robust statistical decisions; Misspecified learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:mateco:v:84:y:2019:i:c:p:144-149

DOI: 10.1016/j.jmateco.2019.07.012

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