Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253470 
Year of Publication: 
2021
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 16 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 73-99
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We establish convergence of beliefs and actions in a class of one-dimensional learning settings in which the agent's model is misspecified, she chooses actions endogenously, and the actions affect how she misinterprets information. Our stochastic-approximation-based methods rely on two crucial features: that the state and action spaces are continuous, and that the agent's posterior admits a one-dimensional summary statistic. Through a basic model with a normal-normal updating structure and a generalization in which the agent's misinterpretation of information can depend on her current beliefs in a flexible way, we show that these features are compatible with a number of specifications of how exactly the agent updates. Applications of our framework include learning by a person who has an incorrect model of a technology she uses or is overconfident about herself, learning by a representative agent who may misunderstand macroeconomic outcomes, as well as learning by a firm that has an incorrect parametric model of demand.
Subjects: 
Misspecified model
Bayesian learning
convergence
Berk-Nash equilibrium
JEL: 
D83
D90
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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