Learning from ambiguous and misspecified models

Marinacci, Massimo and Massari, Filippo (2019) Learning from ambiguous and misspecified models. Journal of Mathematical Economics, 84. pp. 144-149. ISSN 0304-4068

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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.

Item Type:	Article
Uncontrolled Keywords:	ambiguity,learning,misspecified learning,robust statistical decisions,economics and econometrics,applied mathematics ,/dk/atira/pure/subjectarea/asjc/2000/2002
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	22 Jul 2020 02:46
Last Modified:	26 Oct 2022 00:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/76214
DOI:	10.1016/j.jmateco.2019.07.012

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