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Revisions to PCE Inflation Measures: Implications for Monetary Policy

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  • Dean Croushore

    (University of Richmond, Federal Reserve Bank of Philadelphia (Visiting Scholar))

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. We examine the magnitude and patterns of revisions to both PCE inflation rates. We can forecast data revisions in real time from the initial release to the annual revision released in the following year. Policymakers should account for this predictability in setting monetary policy.

Suggested Citation

  • Dean Croushore, 2019. "Revisions to PCE Inflation Measures: Implications for Monetary Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 241-265, October.
    • Handle: RePEc:ijc:ijcjou:y:2019:q:4:a:7
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    References listed on IDEAS

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    Cited by:

    1. Gilbert, Thomas, 2011. "Information aggregation around macroeconomic announcements: Revisions matter," Journal of Financial Economics, Elsevier, vol. 101(1), pages 114-131, July.
    2. Michael Dotsey & Charles I. Plosser, 2012. "Designing monetary policy rules in an uncertain economic environment," Business Review, Federal Reserve Bank of Philadelphia, issue Q1, pages 1-9.
    3. 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.
    4. Tierney, Heather L.R., 2011. "Real-time data revisions and the PCE measure of inflation," Economic Modelling, Elsevier, vol. 28(4), pages 1763-1773, July.
    5. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.
    6. Camila Figueroa & Jorge Fornero & Pablo García, 2019. "Hindsight vs. Real time measurement of the output gap: Implications for the Phillips curve in the Chilean Case," Working Papers Central Bank of Chile 854, Central Bank of Chile.
    7. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    8. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    9. 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, vol. 91(Jul), pages 371-382.
    10. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    11. 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, vol. 91(Jul), pages 349-370.
    12. Bachmann, Rüdiger & Gödl-Hanisch, Isabel & Sims, Eric R., 2022. "Identifying monetary policy shocks using the central bank’s information set," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    13. Heather L. R. Tierney, 2012. "Examining the ability of core inflation to capture the overall trend of total inflation," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 493-514, February.
    14. Tierney, Heather L.R., 2009. "Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data," MPRA Paper 17856, University Library of Munich, Germany.
    15. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    16. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    17. 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.
    18. Kishor, N. Kundan, 2011. "Data revisions in India: Implications for monetary policy," Journal of Asian Economics, Elsevier, vol. 22(2), pages 164-173, April.
    19. Heather L. R. Tierney, 2019. "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 39-63, February.
    20. Kishor, N. Kundan, 2009. "Data Revisions in India and its Implications for Monetary Policy," MPRA Paper 16099, University Library of Munich, Germany.
    21. Pavol Povala & Anna Cieslak, 2012. "Understanding bond risk premia," 2012 Meeting Papers 771, Society for Economic Dynamics.
    22. N. Kundan Kishor & Evan F. Koenig, 2016. "The roles of inflation expectations, core inflation, and slack in real-time inflation forecasting," Working Papers 1613, Federal Reserve Bank of Dallas.
    23. Michael P. Clements & Ana Beatriz Galvão, 2012. "Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 554-562, May.

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    More about this item

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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