Learning and Model Validation

Inkoo Cho and Kenneth Kasa

The Review of Economic Studies, 2015, vol. 82, issue 1, 45-82

Abstract: This paper studies adaptive learning with multiple models. An agent operating in a self-referential environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric specification test. If the current model passes the test, it is used to construct an optimal policy. If it fails the test, a new model is selected. As the rate of coefficient updating decreases, one model becomes dominant, and is used “almost always”. Dominant models can be characterized using the tools of large deviations theory. The analysis is used to address two questions posed by Sargent's Phillips Curve model.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://hdl.handle.net/10.1093/restud/rdu026 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Learning and Model Validation (2011)
Working Paper: Learning and Model Validation (2007)
Working Paper: Learning and Model Validation (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:82:y:2015:i:1:p:45-82

Access Statistics for this article

The Review of Economic Studies is currently edited by Thomas Chaney, Xavier d’Haultfoeuille, Andrea Galeotti, Bård Harstad, Nir Jaimovich, Katrine Loken, Elias Papaioannou, Vincent Sterk and Noam Yuchtman

More articles in The Review of Economic Studies from Review of Economic Studies Ltd
Bibliographic data for series maintained by Oxford University Press ().