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The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan

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  • LUTZ KILIAN
  • SIMONE MANGANELLI

Abstract

We derive a natural generalization of the Taylor rule that links changes in the interest rate to the balance of the risks implied by the dual objective of sustainable economic growth and price stability. This monetary policy rule reconciles economic models of expected utility maximization with the risk management approach to central banking. Within this framework, we formally test and reject the standard assumption of quadratic and symmetric preferences in inflation and output that underlies the derivation of the Taylor rule. Our results suggest that Fed policy decisions under Greenspan were better described in terms of the Fed weighing upside and downside risks to their objectives rather than simply responding to the conditional mean of inflation and of the output gap. Copyright (c) 2008 The Ohio State University.

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Bibliographic Info

Article provided by Blackwell Publishing in its journal Journal of Money, Credit and Banking.

Volume (Year): 40 (2008)
Issue (Month): 6 (09)
Pages: 1103-1129

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Handle: RePEc:mcb:jmoncb:v:40:y:2008:i:6:p:1103-1129

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0022-2879

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  1. Richard Clarida & Jordi Galí & Mark Gertler, 1997. "The science of monetary policy: A new Keynesian perspective," Economics Working Papers 356, Department of Economics and Business, Universitat Pompeu Fabra, revised Apr 1999.
  2. Lutz Kilian & Simone Manganelli, 2007. "Quantifying the Risk of Deflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 561-590, 03.
  3. Lars E.O. Svensson, 2002. "Inflation Targeting: Should It Be Modeled as an Instrument Rule or a Targeting Rule?," NBER Working Papers 8925, National Bureau of Economic Research, Inc.
  4. Fishburn, Peter C, 1977. "Mean-Risk Analysis with Risk Associated with Below-Target Returns," American Economic Review, American Economic Association, vol. 67(2), pages 116-26, March.
  5. Basak, Suleyman & Shapiro, Alexander, 2001. "Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 371-405.
  6. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules And Macroeconomic Stability: Evidence And Some Theory," The Quarterly Journal of Economics, MIT Press, vol. 115(1), pages 147-180, February.
  7. Hall, Alastair R. & Inoue, Atsushi & Jana, Kalidas & Shin, Changmock, 2007. "Information in generalized method of moments estimation and entropy-based moment selection," Journal of Econometrics, Elsevier, vol. 138(2), pages 488-512, June.
  8. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
  9. Taimur Baig & Jörg Decressin & Tarhan Feyzioglu & Manmohan S. Kumar & Chris Faulkner-MacDonagh, 2003. "Deflation," IMF Occasional Papers 221, International Monetary Fund.
  10. Ruge-Murcia, F.J., 2001. "Inflation Targeting Under Asymmetric Preferences," Cahiers de recherche 2001-04, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  11. Holthausen, Duncan M, 1981. "A Risk-Return Model with Risk and Return Measured as Deviations from a Target Return," American Economic Review, American Economic Association, vol. 71(1), pages 182-88, March.
  12. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  13. Alex Cukierman & Anton Muscatelli, 2001. "Do Central Banks have Precautionary Demands for Expansions and for Price Stability?," Working Papers 2002_4, Business School - Economics, University of Glasgow, revised Mar 2002.
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Cited by:
  1. Christian Pierdzioch & Jan-Christoph Rülke & Peter Tillmann, 2013. "Using forecasts to uncover the loss function of FOMC members," MAGKS Papers on Economics 201302, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  2. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
  3. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
  4. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
  5. Baghestani, Hamid & Khallaf, Ashraf, 2012. "Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 222-229.
  6. Matteo Barigozzi & Marco Capasso, 2007. "A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance," LEM Papers Series 2007/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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