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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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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 962.

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Date of creation: 2008
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Handle: RePEc:fip:fedgif:962
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Keywords: Uncertainty ; Econometric models ; Economic forecasting;

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  1. 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.
  2. 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.
  3. Athanasios Orphanides & John C. Williams, 2006. "Monetary Policy with Imperfect Knowledge," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 366-375, 04-05.
  4. 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.
  5. Carboni, Giacomo & Ellison, Martin, 2007. "Learning and the Great Inflation," CEPR Discussion Papers 6250, C.E.P.R. Discussion Papers.
  6. Eric Ghysels & Norman R. Swanson & Myles Callan, 2002. "Monetary Policy Rules with Model and Data Uncertainty," Southern Economic Journal, Southern Economic Association, vol. 69(2), pages 239-265, October.
  7. 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.
  8. 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.
  9. Michael Owyang & Garey Ramey, 2003. "Regime switching and monetary policy measurement," Working Papers 2001-002, Federal Reserve Bank of St. Louis.
  10. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  11. 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.
  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. 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.
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Cited by:

  1. Lubik, Thomas A. & Matthes, Christian, 2014. "Indeterminacy and Learning: An Analysis of Monetary Policy in the Great Inflation," Working Paper 14-2, Federal Reserve Bank of Richmond.
  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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