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Risk Management in Action. Robust monetary policy rules under structured uncertainty

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  • Levine, Paul
  • McAdam, Peter
  • Pearlman, Joseph G.
  • Pierse, Richard

Abstract

Recent interest in ‘Risk Management’ has highlighted the relevance of Bayesian analysis for robust monetary- policy making. This paper sets out a comprehensive methodology for designing policy rules inspired by such considerations. We design rules that are robust with respect to model uncertainty facing both the policy-maker and private sector. We apply our methodology to three simple interest-rate rules: inflation-forecast- based (IFB) rules with a discrete forward horizon, one targeting a discounted sum of forward inflation, and a current wage inflation rule. We use an estimated DSGE model of the euro area and estimated measures of structured exogenous and parameter uncertainty for the exercise. We find that IFB rules with a long horizon perform poorly with or without robust design. Our discounted future targeting rule performs much better, indicating that policy can be highly forward-looking without compromising stabilization. The wage inflation rule dominates whether it is designed to have good robust properties or not. JEL Classification: E52, E37, E58

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 0870.

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Date of creation: Feb 2008
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Handle: RePEc:ecb:ecbwps:20080870

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Keywords: Interest-rate rules; Robustness; structured uncertainty;

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References

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Citations

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Cited by:
  1. Paul Levine & Joseph Pearlman & Peter Welz, 2008. "Robust Inflation-Targeting Rules and the Gains from International Policy Coordination," School of Economics Discussion Papers 0208, School of Economics, University of Surrey.
  2. Paul Levine & Joseph Pearlman, 2007. "Robust Monetary Rules under Unstructured and Structured Model Uncertainty," School of Economics Discussion Papers 0707, School of Economics, University of Surrey.
  3. Paul Levine, 2012. "Policy focus: Monetary policy in an uncertain world: probability models and the design of robust monetary rules," Indian Growth and Development Review, Emerald Group Publishing, vol. 5(1), pages 70-88, April.
  4. Levine, Paul, 2010. "Monetary policy in an uncertain world: Probability models and the design of robust monetary rules," Working Papers 10/72, National Institute of Public Finance and Policy.
  5. Levine, Paul & Pearlman, Joseph, 2010. "Robust monetary rules under unstructured model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 456-471, March.
  6. Levine, Paul & McAdam, Peter & Pearlman, Joseph, 2012. "Probability models and robust policy rules," European Economic Review, Elsevier, vol. 56(2), pages 246-262.
  7. Cogley, Timothy & De Paoli, Bianca & Matthes, Christian & Nikolov, Kalin & Yates, Tony, 2011. "A Bayesian approach to optimal monetary policy with parameter and model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2186-2212.

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