 Informational Herding with Model Misspecification, Second VersionPIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania Abstract: This paper demonstrates that a misspecified model of information processing interferes with long-run learning and allows inefficient choices to persist in the face of contradictory public information. I consider an observational learning environment where agents observe a private signal about a hidden state, and some agents observe the actions of their predecessors. Prior actions aggregate multiple sources of correlated information about the state, and agents face an inferential challenge to distinguish between new and redundant information. When individuals significantly overestimate the amount of new information, beliefs about the state become entrenched and incorrect learning may occur. When individuals sufficiently overestimate the amount of redundant information, beliefs are fragile and learning is incomplete. Learning is complete when agents have an approximately correct model of inference, establishing that the correct model is robust to perturbation. These results have important implications for timing, frequency and strength of policy interventions to facilitate learning. Pages: 32 pages Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:15-022 Access Statistics for this paper More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.