Robust Model Misspeciﬁcation and Paradigm Shifts
This paper studies the forms of model misspeciﬁcation 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 misspeciﬁcation, she uses a threshold rule to switch between models according to how well they ﬁt the data. A model is globally robust if it can persist against every ﬁnite set of competing models and is locally robust if it can persist against every ﬁnite 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, overconﬁdence, and incorrect beliefs about market demand.