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Revisions to PCE inflation measures: implications for monetary policy

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  • Dean Croushore

Abstract

This paper examines the characteristics of the revisions to the inflation rate as measured by the personal consumption expenditures price index both including and excluding food and energy prices. These data series play a major role in the Federal Reserve’s analysis of inflation. ; The author examines the magnitude and patterns of revisions to both PCE inflation rates. The first question he poses is: What do data revisions look like? The author runs a variety of tests to see if the data revisions have desirable or exploitable properties. The second question he poses is related to the first: Can we forecast data revisions in real time? The answer is that it is possible to forecast revisions from the initial release to August of the following year. Generally, the initial release of inflation is too low and is likely to be revised up. Policymakers should account for this predictability in setting monetary policy.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 08-8.

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Date of creation: 2008
Date of revision:
Handle: RePEc:fip:fedpwp:08-8

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Related research

Keywords: Inflation (Finance) ; Monetary policy;

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References

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  1. Aruoba, Boragan, 2005. "Data Revisions Are Not Well-Behaved," CEPR Discussion Papers 5271, C.E.P.R. Discussion Papers.
  2. Dean Croushore & Tom Stark, 1999. "A real-time data set for marcoeconomists: does the data vintage matter?," Working Papers 99-21, Federal Reserve Bank of Philadelphia.
  3. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-19, June.
  4. Anthony Garratt & Shaun P Vahey, 2005. "UK Real-Time Macro Data Characteristics," Birkbeck Working Papers in Economics and Finance 0502, Birkbeck, Department of Economics, Mathematics & Statistics.
  5. Andrew C. Krikelas, 1994. "Revision to payroll employment data: are they predictable?," Economic Review, Federal Reserve Bank of Atlanta, issue Nov, pages 17-29.
  6. William Conrad & Carol Corrado, 1978. "Applications of the Kalman filter to revisions in monthly retail sales estimates," Special Studies Papers 125, Board of Governors of the Federal Reserve System (U.S.).
  7. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
  8. Mork, Knut Anton, 1987. "Ain't Behavin': Forecast Errors and Measurement Errors in Early GNP Estimates," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 165-75, April.
  9. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
  10. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
  11. 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.
  12. Patterson, K. D., 1994. "A state space model for reducing the uncertainty associated with preliminary vintages of data with an application to aggregate consumption," Economics Letters, Elsevier, vol. 46(3), pages 215-222, November.
  13. Patterson, Kerry D & Heravi, Saeed M, 1991. "Data Revisions and the Expenditure Components of GDP," Economic Journal, Royal Economic Society, vol. 101(407), pages 887-901, July.
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Citations

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Cited by:
  1. Tierney, Heather L.R., 2010. "Real-Time Data Revisions and the PCE Measure of Inflation," MPRA Paper 20625, University Library of Munich, Germany.
  2. Tierney, Heather L.R., 2009. "Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation," MPRA Paper 22409, University Library of Munich, Germany, revised Feb 2010.
  3. Pierre L. Siklos & Diana N. Weymark, 2008. "Data Revisions, Gradualism, and US Inflation Pressure in Real Time," Vanderbilt University Department of Economics Working Papers 0816, Vanderbilt University Department of Economics.
  4. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
  5. Kishor, N. Kundan, 2009. "Data Revisions in India and its Implications for Monetary Policy," MPRA Paper 16099, University Library of Munich, Germany.
  6. Tierney, Heather L.R., 2009. "A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data," MPRA Paper 13383, University Library of Munich, Germany, revised 03 Feb 2009.
  7. Kishor, N. Kundan, 2011. "Data revisions in India: Implications for monetary policy," Journal of Asian Economics, Elsevier, vol. 22(2), pages 164-173, April.
  8. Dean Croushore, 2009. "Commentary on Estimating U.S. output growth with vintage data in a state-space framework," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 371-382.
  9. Richard G. Anderson & Charles S. Gascon, 2009. "Estimating U.S. output growth with vintage data in a state-space framework," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 349-370.
  10. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-84, December.
  11. Tierney, Heather L.R., 2009. "Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data," MPRA Paper 17856, University Library of Munich, Germany.
  12. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  13. Gilbert, Thomas, 2011. "Information aggregation around macroeconomic announcements: Revisions matter," Journal of Financial Economics, Elsevier, vol. 101(1), pages 114-131, July.

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