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Model Validation and Learning

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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 '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.

Suggested Citation

  • In-Koo Cho & Ken Kasa, 2012. "Model Validation and Learning," Discussion Papers dp12-07, Department of Economics, Simon Fraser University.
  • Handle: RePEc:sfu:sfudps:dp12-07
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    Cited by:

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    4. Norman, Thomas W.L., 2015. "Learning, hypothesis testing, and rational-expectations equilibrium," Games and Economic Behavior, Elsevier, vol. 90(C), pages 93-105.
    5. Milani, Fabio, 2014. "Learning and time-varying macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 94-114.

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    More about this item

    Keywords

    Learning; Model validation;

    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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