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A Bayesian approach to optimal monetary policy with parameter and model uncertainty

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  • Cogley, Timothy
  • De Paoli, Bianca
  • Matthes, Christian
  • Nikolov, Kalin
  • Yates, Tony

Abstract

This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetary-policy models that include both forward- and backward-looking representations as well as large- and small-scale models. We find an optimal simple Taylor-type rule that accounts for both model and parameter uncertainty. For the most part, backward-looking models are highly fault tolerant with respect to policies optimized for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimized for backward-looking representations. In addition, backward-looking models often have lower posterior probabilities than forward-looking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forward-looking models.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 35 (2011)
Issue (Month): 12 ()
Pages: 2186-2212

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Handle: RePEc:eee:dyncon:v:35:y:2011:i:12:p:2186-2212

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Monetary policy; Bayesian analysis; Statistical decision theory; Quantitative policy modeling;

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References

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  1. Timothy Cogley & Riccardo Colacito & Lars Peter Hansen & Thomas J. Sargent, 2008. "Robustness and U.S. Monetary Policy Experimentation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(8), pages 1599-1623, December.
  2. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393 Elsevier.
  3. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
  4. Timothy Cogley & Riccardo Colacito & Thomas J. Sargent, 2005. "Benefits from U.S. monetary policy experimentation in the days of Samuelson and Solow and Lucas," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
  5. Paul Levine & Peter McAdam & Joseph Pearlman & Richard Pierse, 2008. "Risk Management in Action. Robust monetary policy rules under structured uncertainty," Working Paper Series 870, European Central Bank.
  6. Noah Williams & Lars E.O. Svensson, 2005. "Monetary Policy with Model Uncertainty: Distribution Forecast Targeting," Computing in Economics and Finance 2005 108, Society for Computational Economics.
  7. De Paoli, Bianca, 2009. "Monetary policy and welfare in a small open economy," Journal of International Economics, Elsevier, vol. 77(1), pages 11-22, February.
  8. William Brock & Steven Durlauf & Kenneth West, 2005. "Model uncertainty and policy evaluation: some theory and empirics," Proceedings, Federal Reserve Bank of San Francisco.
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Citations

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Cited by:
  1. Francesca Rondina, 2010. "The role of model uncertainty and learning in the U.S. postwar policy response to oil prices," Working Papers 478, Barcelona Graduate School of Economics.
  2. Francesca Rondina, 2010. "Policy Evaluation and Uncertainty About the Effects of Oil Prices on Economic Activity," Working Papers 522, Barcelona Graduate School of Economics.
  3. Polito, Vito & Spencer, Peter, 2011. "UK Macroeconomic Volatility and the Welfare Costs of Inflation," Cardiff Economics Working Papers E2011/23, Cardiff University, Cardiff Business School, Economics Section.

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