Robust Misspecified Models and Paradigm Shifts
Cuimin Ba (University of Pittsburgh)
Abstract:
This paper studies which misspecified models are likely to persist when decisionmakers compare them with competing models. The main result provides a characterization of such models based on two features that are straightforward to derive from the primitives: the model’s asymptotic accuracy in predicting the equilibrium pattern of observed outcomes and the ‘tightness’ of the prior around such equilibria. Misspecified models can be robust, persisting against a wide range of competing models—including the correct model—despite individuals observing an infinite amount of data. Moreover, simple misspecified models equipped with entrenched priors can be more robust than complex correctly specified models.