What question did this study set out to answer?

The research aims to explore the persistence of misspecified models when compared to other competing models.

April 1, 2026

Robust Misspecified Models

Key Points

The research aims to explore the persistence of misspecified models when compared to other competing models.
Characterization of models based on asymptotic accuracy and prior tightness.
Analytical comparison of misspecified vs competing models.
Evaluation of robustness in light of infinite observational data.
Misspecified models can persist despite the availability of data.
Simple misspecified models with strong priors may outperform complex correctly specified models.

Abstract

This paper studies which misspecified models are likely to persist when decision-makers compare them with competing models. The main result characterizes such models based on two features that can be derived from primitives: The model’s asymptotic accuracy in predicting the equilibrium distribution of observed outcomes and the “tightness” of the prior around such equilibria. Misspecified models can be robust, persisting against any arbitrary competing model—including the true model—despite decision-makers observing an infinite amount of data. Moreover, simple misspecified models equipped with entrenched priors can be more robust than complex correctly specified models. (JEL C11, C52, D11, L82)

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Robust Misspecified Models

Key Points

Abstract

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