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Uncertainty Over Models and Data: The Rise and Fall of American Inflation

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  • SETH PRUITT

Abstract

An economic agent who is uncertain of her model updates her beliefs in response to the data. The updating is sensitive to measurement error which, in many cases of macroeconomic interest, is apparent from the process of data revision. I make this point through simple illustrations and then analyze a recent model of the Federal Reserve's role in U.S. inflation. The existing model succeeds at fitting inflation to optimal policy, but fails to link inflation to the economic trade-off at the heart of the story. I modify the model to account for data uncertainty and find that doing so ameliorates the existing problems. This suggests that the Fed's model uncertainty is largely overestimated by ignoring data uncertainty. Consequently, now there is an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade-off.

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Bibliographic Info

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

Volume (Year): 44 (2012)
Issue (Month): (03)
Pages: 341-365

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Handle: RePEc:mcb:jmoncb:v:44:y:2012:i::p:341-365

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

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  1. Myles Callan & Eric Ghysels & Norman R. Swanson, 1998. "Monetary Policy Rules with Model and Data Uncertainty," CIRANO Working Papers 98s-40, CIRANO.
  2. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
  3. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
  4. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
  5. Owyang, Michael T. & Ramey, Garey, 2001. "Regime Switching and Monetary Policy Measurement," University of California at San Diego, Economics Working Paper Series qt24q32688, Department of Economics, UC San Diego.
  6. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
  7. Athanasios Orphanides & John C. Williams, 2005. "Monetary policy with imperfect knowledge," Working Paper Series 2005-17, Federal Reserve Bank of San Francisco.
  8. Carboni, Giacomo & Ellison, Martin, 2007. "Learning and the Great Inflation," CEPR Discussion Papers 6250, C.E.P.R. Discussion Papers.
  9. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  10. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1107-1125.
  11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  12. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
  13. Jean Boivin, 2005. "Has US Monetary Policy Changed? Evidence from Drifting Coefficients and Real-Time Data," NBER Working Papers 11314, National Bureau of Economic Research, Inc.
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Cited by:
  1. Christian Matthes & Thomas Lubik, 2013. "Indeterminacy and Learning: An Analysis of Monetary Policy in the Great Inflation," 2013 Meeting Papers 973, Society for Economic Dynamics.
  2. Lafuente, Juan A. & Pérez, Rafaela & Ruiz, Jesús, 2014. "Time-varying inflation targeting after the nineties," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 400-408.

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