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Methods Available to Monetary Policy Makers to Deal with Uncertainty

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  • Katerina Smidkova

    (Czech National Bank)

Abstract

Three sources – research on monetary policy under uncertainty, the managerial literature, and the real-life strategies of five inflation targeters – have been used to survey methods that are available to monetary policy makers to deal with uncertainty. The methods have been compared within a framework that is based on a decision matrix. The comparative framework has been designed in order to encompass different representations of uncertainty employed by various central banks. The results of comparative analysis suggest that central banks use models, intuition, judgement as well as traditional managerial methods to deal with uncertainty. This finding helps understanding why economic research cannot fully explain differences between monetary policy actions and outcomes of model simulations. The results of the comparative analysis also suggest that central banks have not so far fully utilised the whole spectrum of methods available to them. Economic research, other banks’ strategies as well as decision analysis may be interesting sources of inspiration when designing the decision-making process. It is emphasised that central banks introducing inflation targeting should pay equal attention to both building their forecasting models as well as selecting methods to deal with uncertainty. In the case of emerging economies where uncertainty can be much higher than in advanced economies, neglecting uncertainty may increase probability of policy errors significantly.

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Bibliographic Info

Paper provided by EconWPA in its series Macroeconomics with number 0310002.

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Length: 38 pages
Date of creation: 06 Oct 2003
Date of revision:
Handle: RePEc:wpa:wuwpma:0310002

Note: Type of Document - ; pages: 38 . The paper has been prepared for the conference “Forecasting in a Central Bank”, Bank of England, August 2003, London.
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Web page: http://128.118.178.162

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Keywords: Inflation targeting Uncertainty Decision matrix Survey of methods;

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References

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  1. Svensson, Lars E O, 2004. "Optimal Policy with Low-Probability Extreme Events," CEPR Discussion Papers 4218, C.E.P.R. Discussion Papers.
  2. Peter Isard & Douglas Laxton & Ann-Charlotte Eliasson, 1999. "Simple Monetary Policy Rules Under Model Uncertainty," International Tax and Public Finance, Springer, vol. 6(4), pages 537-577, November.
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  7. Svensson, Lars E.O., 1997. "Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets," Seminar Papers 615, Stockholm University, Institute for International Economic Studies.
  8. Katerina Smidkova, 2003. "Targeting Inflation under Uncertainty: Policy Makers' Perspective," Research and Policy Notes 2003/02, Czech National Bank, Research Department.
  9. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
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  14. Orphanides, Athanasios, 2003. "Monetary policy evaluation with noisy information," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 605-631, April.
  15. Tiff Macklem, 2002. "Information and Analysis for Monetary Policy: Coming to a Decision," Bank of Canada Review, Bank of Canada, vol. 2002(Summer), pages 11-18.
  16. Lavan Mahadeva & Katerina Smidkova, 2004. "Modelling transmission mechanism of monetary policy in the Czech Republic," Macroeconomics 0402032, EconWPA.
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Cited by:
  1. Jürgen Stark & Alexander Jung & Francesco Paolo Mongelli, 2012. "Normal Times versus Crisis Times," CESifo DICE Report, Ifo Institute for Economic Research at the University of Munich, vol. 10(1), pages 03-09, 04.
  2. Kateøina Šmídková, 2005. "How Inflation Targeters (Can) Deal with Uncertainty," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 55(7-8), pages 316-332, July.

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