Model Validation and Learning
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 from a fixed set of models. 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 applied to Sargent's (1999) Phillips Curve model.
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Journal of Econometrics,
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- William Brock & Steven Durlauf & Kenneth West, 2005. "Model uncertainty and policy evaluation: some theory and empirics," Proceedings, Federal Reserve Bank of San Francisco.
- Brock,W.A. & Durlauf,S.N. & West,K.D., 2004. "Model uncertainty and policy evaluation : some theory and empirics," Working papers 19, Wisconsin Madison - Social Systems.
- William A. Brock & Steven N. Durlauf & Kenneth D. West, 2004. "Model Uncertainty and Policy Evaluation: Some Theory and Empirics," NBER Working Papers 10916, National Bureau of Economic Research, Inc.
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