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Time-series measures of core inflation

Author

Listed:
  • Edward N. Gamber

    () (Congressional Budget Office)

  • Julie K. Smith

    () (Lafayette College)

Abstract

Most papers examining the measurement of core inflation, such as the weighted median, have focused on cross-section information in the disaggregated inflation data. This paper improves on the literature by introducing new measures, based on a definition of core inflation as the best predictor of future inflation that exploits the time-series information in the disaggregated inflation data. Exploiting the time-series information in disaggregated or component inflation data produces better forecasts. Additionally, the best new measure comes from jointly estimating the optimal weights instead of imposing weights based on the persistence of the components or the underlying factors estimated by principal components.

Suggested Citation

  • Edward N. Gamber & Julie K. Smith, 2016. "Time-series measures of core inflation," Working Papers 2016-008, The George Washington University, Department of Economics, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2016-008
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    File URL: https://www2.gwu.edu/~forcpgm/2016-008.pdf
    File Function: First version, 2016
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    References listed on IDEAS

    as
    1. Michael Pedersen, 2009. "An Alternative Core Inflation Measure," German Economic Review, Verein für Socialpolitik, vol. 10, pages 139-164, May.
    2. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    3. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    4. Cogley, Timothy, 2002. "A Simple Adaptive Measure of Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 94-113, February.
    5. Benati, Luca, 2008. "Investigating inflation persistence across monetary regimes," Working Paper Series 851, European Central Bank.
    6. Bagliano, Fabio C. & Morana, Claudio, 2003. "Measuring US core inflation: A common trends approach," Journal of Macroeconomics, Elsevier, vol. 25(2), pages 197-212, June.
    7. José R. Maria, 2004. "On the Use of the First Principal Component as a Core Inflation Indicator," Working Papers w200403, Banco de Portugal, Economics and Research Department.
    8. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters,in: Monetary Policy, pages 195-219 National Bureau of Economic Research, Inc.
    9. Franck Sédillot & Hervé Le Bihan, 2002. "Implementing and interpreting indicators of core inflation: the case of France," Empirical Economics, Springer, vol. 27(3), pages 473-497.
    10. Juan-Luis Vega & Mark A. Wynne, 2003. "A First Assessment of Some Measures of Core Inflation for the Euro Area," German Economic Review, Verein für Socialpolitik, vol. 4, pages 269-306, August.
    11. Luca Benati, 2008. "Investigating Inflation Persistence Across Monetary Regimes," The Quarterly Journal of Economics, Oxford University Press, vol. 123(3), pages 1005-1060.
    12. Alan K. Detmeister, 2011. "The usefulness of core PCE inflation measures," Finance and Economics Discussion Series 2011-56, Board of Governors of the Federal Reserve System (U.S.).
    13. Smith, Julie K, 2004. "Weighted Median Inflation: Is This Core Inflation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 253-263, April.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Core inflation; inflation; forecasting; disaggregated components; principal components;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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