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Learning and Model Validation

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  • In-Koo Cho
  • Kenneth Kasa

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.

Suggested Citation

  • In-Koo Cho & Kenneth Kasa, 2015. "Learning and Model Validation," Review of Economic Studies, Oxford University Press, vol. 82(1), pages 45-82.
    • Handle: RePEc:oup:restud:v:82:y:2015:i:1:p:45-82
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    File URL: http://hdl.handle.net/10.1093/restud/rdu026
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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.
    2. Shea, Paul, 2015. "Red herrings and revelations: does learning about a new variable worsen forecasts?," Economic Modelling, Elsevier, vol. 49(C), pages 395-406.
    3. William Branch & George W. Evans, 2007. "Model Uncertainty and Endogenous Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 207-237, April.
    4. Häfner, Samuel, 2018. "Stable biased sampling," Games and Economic Behavior, Elsevier, vol. 107(C), pages 109-122.
    5. Hansen, Lars Peter & Sargent, Thomas J., 2007. "Recursive robust estimation and control without commitment," Journal of Economic Theory, Elsevier, vol. 136(1), pages 1-27, September.
    6. Pierpaolo Battigalli & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Thomas Sargent, 2016. "A Framework for the Analysis of Self-Confi rming Policies," Working Papers 573, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Agnieszka Markiewicz, 2012. "Model Uncertainty And Exchange Rate Volatility," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 815-844, August.
    8. Hommes, C.H. & Zhu, M., 2016. "Behavioral Learning Equilibria, Persistence Amplification & Monetary Policy," CeNDEF Working Papers 16-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    9. Branch, William A. & Gasteiger, Emanuel, 2019. "Endogenously (non-)Ricardian beliefs," ECON WPS - Vienna University of Technology Working Papers in Economic Theory and Policy 03/2019, Vienna University of Technology, Institute for Mathematical Methods in Economics, Research Group Economics (ECON).
    10. Berardi, Michele, 2015. "On the fragility of sunspot equilibria under learning and evolutionary dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 251-265.
    11. Georges, Christophre, 2008. "Bounded memory, overparameterized forecast rules, and instability," Economics Letters, Elsevier, vol. 98(2), pages 129-135, February.
    12. Weidong Tian & Junya Jiang & Weidong Tian, 2017. "Model Uncertainty Effect on Asset Prices," International Review of Finance, International Review of Finance Ltd., vol. 17(2), pages 205-233, June.

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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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