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Core Inflation and Trend Inflation

Author

Listed:
  • James H. Stock

    (Harvard University and NBER)

  • Mark W. Watson

    (Princeton University and NBER)

Abstract

This paper examines empirically whether the measurement of trend inflation can be improved by using disaggregated data on sectoral inflation to construct indexes akin to core inflation but with a time-varying distributed lags of weights, where the sectoral weight depends on the timevarying volatility and persistence of the sectoral inflation series and on the comovement among sectors. The modeling framework is a dynamic factor model with time-varying coefficients and stochastic volatility as in Del Negro and Otrok (2008), and is estimated using U.S. data on seventeen components of the personal consumption expenditure inflation index.

Suggested Citation

  • James H. Stock & Mark W. Watson, 2016. "Core Inflation and Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 770-784, October.
  • Handle: RePEc:tpr:restat:v:98:y:2016:i:4:p:770-784
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    References listed on IDEAS

    as
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. The Fed: From forward guidance to data dependence
      by Steve Cecchetti and Kim Schoenholtz in Money, Banking and Financial Markets on 2015-12-14 19:45:45

    Citations

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    Cited by:

    1. Andrew Foerster & Andreas Hornstein & Pierre-Daniel Sarte & Mark W. Watson, 2019. "Aggregate Implications of Changing Sectoral Trends," NBER Working Papers 25867, National Bureau of Economic Research, Inc.
    2. Tobias Hartl & Rolf Tschernig & Enzo Weber, 2020. "Fractional trends in unobserved components models," Papers 2005.03988, arXiv.org, revised May 2020.
    3. Saeed Zaman, 2019. "Cyclical versus Acyclical Inflation: A Deeper Dive," Economic Commentary, Federal Reserve Bank of Cleveland, issue September.
    4. Andrew Foerster & Andreas Hornstein & Pierre-Daniel Sarte & Mark W. Watson, 2019. "Aggregate Implications of Changing Sectoral Trends," NBER Working Papers 25867, National Bureau of Economic Research, Inc.
    5. Mazumder, Sandeep, 2017. "Output gains from accelerating core inflation," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 63-74.
    6. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    7. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    8. Jose Luis Nolazco & Pablo Pincheira & Jorge Selaive, 2016. "The evasive predictive ability of core inflation," Working Papers 15/34, BBVA Bank, Economic Research Department.
    9. Ellis W. Tallman & Saeed Zaman, 2015. "Forecasting Inflation: Phillips Curve Effects on Services Price Measures," Working Papers (Old Series) 1519, Federal Reserve Bank of Cleveland, revised 14 Oct 2015.
    10. Aleksandra Hałka & Grzegorz Szafrański, 2018. "What core inflation indicators measure?," NBP Working Papers 294, Narodowy Bank Polski, Economic Research Department.
    11. Egorov D.A. (Егоров, Д.А.) & Perevyshina E.A. (Перевышина, Е.А.), 2016. "Modelling of Inflationary Processes in Russia
      [Моделирование Инфляционных Процессов В России]
      ," Working Papers 2138, Russian Presidential Academy of National Economy and Public Administration.
    12. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
    13. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 442-457.
    14. Baxa Jaromír & Plašil Miroslav & Vašíček Bořek, 2017. "Inflation and the steeplechase between economic activity variables: evidence for G7 countries," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(1), pages 1-42, January.
    15. Manopimoke, Pym & Limjaroenrat, Vorada, 2017. "Trend inflation estimates for Thailand from disaggregated data," Economic Modelling, Elsevier, vol. 65(C), pages 75-94.
    16. González-Astudillo, Manuel, 2019. "An output gap measure for the euro area: Exploiting country-level and cross-sectional data heterogeneity," European Economic Review, Elsevier, vol. 120(C).
    17. Forbes, Kristin & Kirkham, Lewis & Theodoridis, Konstantinos, 2017. "A trendy approach to UK inflation dynamics," Discussion Papers 49, Monetary Policy Committee Unit, Bank of England.
    18. John D. Burger & Francis E. Warnock & Veronica Cacdac Warnock, 2019. "The Natural Level of Capital Flows," NBER Working Papers 26184, National Bureau of Economic Research, Inc.
    19. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    20. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    21. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.

    More about this item

    Keywords

    inflation forecasts; non-Gaussian state space; time-varying parameters; dissagregated prices;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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