Robust Model Misspeci?cation and Paradigm Shifts

Cuimin Ba

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: This paper studies the forms of model misspeci?cation that are likely to persist when compared with competing models. I consider an agent using a subjective model to learn about an action-dependent outcome distribution. Aware of potential model misspeci?cation, she uses a threshold rule to switch between models according to how well they ?t the data. A model is globally robust if it can persist against every ?nite set of competing models and is locally robust if it can persist against every ?nite set of nearby competing models. The main result provides simple characterizations of globally robust and locally robust models based on the set of Berk-Nash equilibria they induce. I then apply the results to examples including risk underestimation, overcon?dence, and incorrect beliefs about market demand.

Keywords: misspeci?ed Bayesian learning; competing models; robust misspeci?ca-tion; Berk-Nash equilibrium (search for similar items in EconPapers)
Pages: 49 pages
Date: 2021-06-23
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Citations: View citations in EconPapers (3)

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