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Avoiding Nash inflation: Bayesian and robust responses to model uncertainty

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  • Robert J. Tetlow
  • Peter von zur Muehlen

Abstract

In his 1999 monograph The Conquest of American Inflation Tom Sargent describes how a policymaker, who applies a constant-gain algorithm in estimating the Phillips curve, can fall into the grip of an induction problem: concluding on the basis of reduced-form evidence that the trade-off between inflation and output is more favorable than it actually is. This results in oscillations between periods of disinflation and reflation. The problem arises because the policymaker is naive about possible misspecification, her role in creating that misspecification, and its role in policy design. In particular, while her use of a constant-gain algorithm admits the possibility that her model may be misspecified, she does not take this into consideration when designing policy. In this paper, we relax this assumption. We derive five policy rules which treat possible misspecification in three different ways. First, the linear-quadratic Gaussian (LQG) rule exhibits the familiar pattern of escape dynamics described by Sargent. We show a rule that takes uncertainty seriously, but in a Bayesian fashion, does no better. Finally, we consider three rules that are robust in the sense of Knight. The robust rules do a worse job than the LQG approach, and sometimes a lot worse. This is so even though the induction problem faced by the policymaker provides a prima facie case for being robust. We conclude that there appears to be no obvious tool that can be applied mechanically to alleviate the induction problem. A corollary of this finding is that Sargent's story for the inflation of the 1970s is robust to relaxing a key assumption in the original monograph.

Suggested Citation

  • Robert J. Tetlow & Peter von zur Muehlen, 2002. "Avoiding Nash inflation: Bayesian and robust responses to model uncertainty," Finance and Economics Discussion Series 2002-9, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2002-9
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    Cited by:

    1. Thomas Sargent & Noah Williams & Tao Zha, 2009. "The Conquest of South American Inflation," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 211-256, April.
    2. Tetlow, Robert J. & von zur Muehlen, Peter, 2009. "Robustifying learnability," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 296-316, February.
    3. Andrew T. Levin & Alexei Onatski & John Williams & Noah M. Williams, 2006. "Monetary Policy Under Uncertainty in Micro-Founded Macroeconometric Models," NBER Chapters,in: NBER Macroeconomics Annual 2005, Volume 20, pages 229-312 National Bureau of Economic Research, Inc.
    4. Qin, Li & Sidiropoulos, Moïse & Spyromitros, Eleftherios, 2013. "Robust monetary policy under model uncertainty and inflation persistence," Economic Modelling, Elsevier, vol. 30(C), pages 721-728.
    5. 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.
    6. Zampolli, Fabrizio, 2006. "Optimal monetary policy in a regime-switching economy: The response to abrupt shifts in exchange rate dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1527-1567.
    7. Keith Kuester & Volker Wieland, 2010. "Insurance Policies for Monetary Policy in the Euro Area," Journal of the European Economic Association, MIT Press, vol. 8(4), pages 872-912, June.
    8. David Kendrick & Hans Amman, 2006. "A Classification System for Economic Stochastic Control Models," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 453-481, June.
    9. Fidel Gonzalez & Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Response of the Control to Changes in the “Free” Parameter Conditional on the Character of Nature," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 223-238, March.
    10. Tetlow, Robert J. & Ironside, Brian, 2006. "Real-time model uncertainty in the United States: the Fed from 1996-2003," Working Paper Series 610, European Central Bank.
    11. Georges, Christophre, 2006. "Learning with misspecification in an artificial currency market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(1), pages 70-84, May.
    12. Martin Ellison & Liam Graham & Jouko Vilmunen, 2006. "Strong Contagion with Weak Spillovers," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(2), pages 263-283, April.
    13. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    14. Gonzalez F. & Rodriguez A. & Gonzalez-Garcia J.R., 2005. "Uncertainty about the Persistence of Periods with Large Price Shocks and the Optimal Reaction of the Monetary Authority," Computing in Economics and Finance 2005 402, Society for Computational Economics.
    15. Meixing DAI & Eleftherios SPYROMITROS, 2008. "Monetary policy, asset prices and model uncertainty," Working Papers of BETA 2008-15, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    16. Arnulfo Rodríguez & Fidel González & Jesús R. González García, 2007. "Uncertainty about the Persistence of Cost-Push Shocks and the Optimal Reaction of the Monetary Authority," Working Papers 2007-05, Banco de México.

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    Keywords

    Inflation (Finance) ; Econometric models;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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