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Learning and Model Validation

Author

Listed:
  • Kenneth Kasa

    (Simon Fraser University)

  • In-Koo Cho

    (University of Illinois)

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 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.

Suggested Citation

  • Kenneth Kasa & In-Koo Cho, 2011. "Learning and Model Validation," 2011 Meeting Papers 1086, Society for Economic Dynamics.
    • Handle: RePEc:red:sed011:1086
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E59 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Other

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