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Learning and Model Validation

  • Kenneth Kasa

    (Simon Fraser University)

  • In-Koo Cho

    (University of Illinois)

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 âalmost alwaysâ. 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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File URL: https://www.economicdynamics.org/meetpapers/2011/paper_1086.pdf
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Paper provided by Society for Economic Dynamics in its series 2011 Meeting Papers with number 1086.

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Date of creation: 2011
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Handle: RePEc:red:sed011:1086
Contact details of provider: Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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  14. Timothy Cogley & Riccardo Colacito & Thomas J. Sargent, 2005. "Benefits from U.S. monetary policy experimentation in the days of Samuelson and Solow and Lucas," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
  15. Chen, Xiaohong & White, Halbert, 1998. "Nonparametric Adaptive Learning with Feedback," Journal of Economic Theory, Elsevier, vol. 82(1), pages 190-222, September.
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  17. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
  18. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
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