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Robustifying learnability

Author

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  • Tetlow, Robert J.
  • von zur Muehlen, Peter
Abstract
In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra [2002. Learning about monetary policy rules. Journal of Monetary Economics 49 (6), 1105-1139] and Evans and Honkapohja [2006. Monetary Policy, Expectations, and Commitment, Scandinavian Journal of Economics 108, 15-38], have made significant headway in establishing certain features of monetary policy rules that facilitate learning. However a treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper provides such a treatment. We begin with the notion that because the profession has yet to settle on a consensus model of the economy, it is unreasonable to expect private agents to have collective rational expectations. We assume that agents have only an approximate understanding of the workings of the economy and that their learning the reduced forms of the economy is subject to potentially destabilizing perturbations. The issue is then whether a central bank can design policy to account for perturbations and still assure the learnability of the model. We provide two examples one of which-the canonical New Keynesian business cycle model-serves as a test case. For different parameterizations of a given policy rule, we use structured singular value analysis (from robust control theory) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE. In addition, we study the cost, in terms of performance in the steady state of a central bank that acts to robustify learnability on the transition path to REE.

Suggested Citation

  • Tetlow, Robert J. & von zur Muehlen, Peter, 2009. "Robustifying learnability," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 296-316, February.
    • Handle: RePEc:eee:dyncon:v:33:y:2009:i:2:p:296-316
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    Cited by:

    1. Spahn Peter, 2009. "The New Keynesian Microfoundation of Macroeconomics," Review of Economics, De Gruyter, vol. 60(3), pages 181-203, December.
    2. Martin Ellison & Thomas J. Sargent, 2012. "A Defense Of The Fomc," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1047-1065, November.
    3. GEORGE W. EVANS & BRUCE McGOUGH, 2007. "Optimal Constrained Interest-Rate Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1335-1356, September.
    4. Mostafavi, Moeen & Shakouri G., Hamed & Fatehi, Ali-Reza, 2010. "Why the determinacy condition is a weak criterion in rational expectations models," MPRA Paper 28320, University Library of Munich, Germany.
    5. Lubik, Thomas A. & Matthes, Christian, 2016. "Indeterminacy and learning: An analysis of monetary policy in the Great Inflation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 85-106.
    6. George W. Evans & Seppo Honkapohja, 2009. "Expectations, Learning and Monetary Policy: An Overview of Recent Research," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 2, pages 027-076, Central Bank of Chile.
    7. Mostafavi, Moeen & Fatehi, Ali-Reza & Shakouri G., Hamed & Von zur Muehlen, Peter, 2011. "A predictive multi-agent approach to model systems with linear rational expectations," MPRA Paper 35351, University Library of Munich, Germany, revised 11 Dec 2011.
    8. Ludwig, Alexander & Zimper, Alexander, 2014. "Biased Bayesian learning with an application to the risk-free rate puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 79-97.
    9. Peter von zur Muehlen, 2022. "Prices and Taxes in a Ramsey Climate Policy Model under Heterogeneous Beliefs and Ambiguity," Economies, MDPI, vol. 10(10), pages 1-56, October.

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    More about this item

    Keywords

    Monetary policy Learning E-stability Learnability Robust control;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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