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Stochastic Optimisation and Worst Case Analysis in Monetary Policy Design

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  • S. Zakovic
  • V. Wieland
  • B. Rustem

Abstract

In this paper, we show how stochastic optimisation and worst-case analysis can be used together in order to provide central banks with a straightforward tool for selecting a policy rule that limits worst-case outcomes while at the same time providing reasonably good performance on average. We conduct this analysis within a simple estimated model of the euro area with adaptive expectations. In particular, we consider not only uncertainty due to additive shocks but also uncertainty with respect to all the parameters of the model, including multiplicative parameters and potential nonlinearities in the inflation-output relationship. In terms of monetary policy we focus on the optimal choice of response coefficients in a Taylor-style interest rate rule that responds to inflation and the output gap and we evaluate the performance of this type of rule by means of a standard quadratic loss function in output and inflation. We then compare the rules obtained by the two different methods by comparing their respective performance in the worst-case scenario as well as the overall expected performance given the empirical probability distributions.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 213.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:213

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Keywords: robust decisions; worst case analysis; expected value optimisation;

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References

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  1. Williams, John C. & Levin, Andrew T. & Wieland, Volker, 2001. "The performance of forecast-based monetary policy rules under model uncertainty," Working Paper Series 0068, European Central Bank.
  2. Svensson, Lars E O, 1996. "Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets," CEPR Discussion Papers 1511, C.E.P.R. Discussion Papers.
  3. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
  4. Andrew Levin & Volker Wieland & John C. Williams, 1998. "Robustness of Simple Monetary Policy Rules under Model Uncertainty," NBER Working Papers 6570, National Bureau of Economic Research, Inc.
  5. Peter Isard & Douglas Laxton & Ann-Charlotte Eliasson, 1999. "Simple Monetary Policy Rules Under Model Uncertainty," Computing in Economics and Finance 1999 841, Society for Computational Economics.
  6. Laurence M. Ball, 1999. "Policy Rules for Open Economies," NBER Chapters, in: Monetary Policy Rules, pages 127-156 National Bureau of Economic Research, Inc.
  7. Robert J. Tetlow & Peter von zur Muehlen, 2000. "Robust monetary policy with misspecified models: does model uncertainty always call for attenuated policy?," Finance and Economics Discussion Series 2000-28, Board of Governors of the Federal Reserve System (U.S.).
  8. Alexei Onatski & James H. Stock, 2000. "Robust Monetary Policy Under Model Uncertainty in a Small Model of the U.S. Economy," NBER Working Papers 7490, National Bureau of Economic Research, Inc.
  9. Orphanides, Athanasios, 2003. "Monetary policy evaluation with noisy information," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 605-631, April.
  10. Lars Peter Hansen & Thomas J. Sargent & Thomas D. Tallarini Jr., 1997. "Robust Permanent Income and Pricing," Levine's Working Paper Archive 596, David K. Levine.
  11. Orphanides, Athanasios & Wieland, Volker, 2000. "Inflation zone targeting," European Economic Review, Elsevier, vol. 44(7), pages 1351-1387, June.
  12. Peter von zur Muehlen, 2001. "Activist vs. non-activist monetary policy: optimal rules under extreme uncertainty," Finance and Economics Discussion Series 2001-02, Board of Governors of the Federal Reserve System (U.S.).
  13. repec:cup:macdyn:v:6:y:2002:i:1:p:111-44 is not listed on IDEAS
  14. Christopher A. Sims, 2001. "Pitfalls of a Minimax Approach to Model Uncertainty," American Economic Review, American Economic Association, vol. 91(2), pages 51-54, May.
  15. Giannoni, Marc P., 2002. "Does Model Uncertainty Justify Caution? Robust Optimal Monetary Policy In A Forward-Looking Model," Macroeconomic Dynamics, Cambridge University Press, vol. 6(01), pages 111-144, February.
  16. Karakitsos, E. & Rustem, B., 1984. "Optimally derived fixed rules and indicators," Journal of Economic Dynamics and Control, Elsevier, vol. 8(1), pages 33-64, October.
  17. Rustem, Berc, 1994. "Stochastic and robust control of nonlinear economic systems," European Journal of Operational Research, Elsevier, vol. 73(2), pages 304-318, March.
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Citations

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Cited by:
  1. Q. Farooq Akram & Yakov Ben-Haim & Øyvind Eitrheim, 2006. "Managing uncertainty through robust-satisficing monetary policy," Working Paper 2006/10, Norges Bank.
  2. Gadi Barlevy, 2009. "Policymaking under uncertainty: Gradualism and robustness," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q II, pages 38-55.
  3. Küster, Keith & Wieland, Volker, 2005. "Insurance policies for monetary policy in the Euro area," CFS Working Paper Series 2005/13, Center for Financial Studies (CFS).
  4. Kai Leitemo & Ulf Söderstrom, 2005. "Robust Monetary Policy in a Small Open Economy," Working Papers 290, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  5. Lim, G.C. & McNelis, Paul D., 2007. "Inflation targeting, learning and Q volatility in small open economies," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3699-3722, November.
  6. Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M., 2007. "Worst-case estimation for econometric models with unobservable components," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3330-3354, April.
  7. Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Timing of Model Uncertainty," Computational Economics, Society for Computational Economics, vol. 24(3), pages 209-221, July.

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