Working Paper: CEPR ID: DP17884
Authors: Aislinn Bohren; Daniel Hauser
Abstract: We link two approaches to biased belief formation: non-Bayesian updating rules and model misspecification. Each approach has advantages: updating rules transparently capture the underlying bias and are identifiable from belief data; misspecified models are `complete' and amenable to general analysis. We show that misspecified models can be decomposed into an updating rule and forecast of anticipated future beliefs. We derive necessary and sufficient conditions for an updating rule and forecast to have a misspecified model representation, show the representation is unique, and construct it. This highlights the belief restrictions implicit in the misspecified model approach. Finally, we explore two ways to select forecasts---introspection-proof and naive consistent---and derive when a representation of each exists.
Keywords: model misspecification; belief formation; learning; nonbayesian updating; heuristics
JEL Codes: D83
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonbayesian updating rules (C11) | misspecified models (C50) |
| prior belief is contained within the relative interior of the convex hull of posterior beliefs (D80) | updating rule is responsive (Y10) |
| conditions for a forecast to be plausible and satisfy the no unexpected beliefs condition (C53) | joint representation with an updating rule (C59) |
| misspecified model (C52) | prospective bias captured by the forecast and retrospective bias captured by the updating rule (C53) |
| introspectionproof condition (D80) | stability of beliefs over time (D15) |
| biased belief formation (D91) | discriminatory behavior (J71) |