IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_593_20.html
   My bibliography  Save this paper

Alternative measures of underlying inflation in the euro area

Author

Listed:
  • Cristina Conflitti

    (Bank of Italy)

Abstract

This paper proposes two measures of underlying inflation for euro area as an alternative to the Harmonized Index of Consumer Prices excluding Food and Energy. The first measure, called the Core cycle measure, is constructed by using a Phillips curve model to distinguish disaggregated prices that respond to the economic cycle (procyclical), from those which do not (acyclical). The second measure, called the Common core measure, is constructed using a factor model to remove components that are subject to large or unusual price changes, which are unlikely to be related to the underlying trend of inflation because of their idiosyncratic nature. Each measure has merits and shortcomings, suggesting that they should be taken together to assess inflation developments.

Suggested Citation

  • Cristina Conflitti, 2020. "Alternative measures of underlying inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 593, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_593_20
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2020-0593/QEF_593_20.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cristadoro, Riccardo & Forni, Mario & Reichlin, Lucrezia & Veronese, Giovanni, 2005. "A Core Inflation Indicator for the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 539-560, June.
    2. Laurence Ball & Sandeep Mazumder, 2020. "The Nonpuzzling Behavior of Median Inflation," Central Banking, Analysis, and Economic Policies Book Series, in: Gonzalo Castex & Jordi Galí & Diego Saravia (ed.),Changing Inflation Dynamics,Evolving Monetary Policy, edition 1, volume 27, chapter 3, pages 049-070, Central Bank of Chile.
    3. Altissimo, Filippo & Mojon, Benoit & Zaffaroni, Paolo, 2009. "Can aggregation explain the persistence of inflation?," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 231-241, March.
    4. Cristina Conflitti and Matteo Luciani, 2019. "Oil Price Pass-through into Core Inflation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    5. Alessandro Brunetti, 2010. "The decomposition of the chained price index rate of change: generalization and interpretative effectiveness," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(1), pages 17-34, April.
    6. Matteo Luciani & Riccardo Trezzi, 2019. "Comparing Two Measures of Core Inflation: PCE Excluding Food & Energy vs. the Trimmed Mean PCE Index," FEDS Notes 2019-08-02-1, Board of Governors of the Federal Reserve System (U.S.).
    7. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    8. Matteo Luciani, 2020. "Common and Idiosyncratic Inflation," Finance and Economics Discussion Series 2020-024, Board of Governors of the Federal Reserve System (U.S.).
    9. Ricardo Reis & Mark W. Watson, 2010. "Relative Goods' Prices, Pure Inflation, and the Phillips Correlation," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(3), pages 128-157, July.
    10. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    11. Rostagno, Massimo & Altavilla, Carlo & Carboni, Giacomo & Lemke, Wolfgang & Motto, Roberto & Saint Guilhem, Arthur & Yiangou, Jonathan, 2019. "A tale of two decades: the ECB’s monetary policy at 20," Working Paper Series 2346, European Central Bank.
    12. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    2. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.
    3. Guillermo Carlomagno & Jorge Fornero & Andrés Sansone, 2021. "Toward a general framework for constructing and evaluating core inflation measures," Working Papers Central Bank of Chile 913, Central Bank of Chile.
    4. Sara Serra & João Quelhas, 2023. "The inflation process in Portugal: the role of price spillovers," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barigozzi, Matteo & Hallin, Marc & Luciani, Matteo & Zaffaroni, Paolo, 2024. "Inferential theory for generalized dynamic factor models," Journal of Econometrics, Elsevier, vol. 239(2).
    2. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    3. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    4. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    5. Cristina Conflitti and Matteo Luciani, 2019. "Oil Price Pass-through into Core Inflation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    6. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    7. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    8. Alberto Humala & Gabriel Rodr�guez, 2012. "A factorial decomposition of inflation in Peru: an alternative measure of core inflation," Applied Economics Letters, Taylor & Francis Journals, vol. 19(14), pages 1331-1334, September.
    9. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    10. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    11. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    12. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    13. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    14. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised May 2022.
    15. Luke Hartigan & James Morley, 2020. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 271-293, September.
    16. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    17. Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.
    18. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    19. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    20. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).

    More about this item

    Keywords

    core inflation; disaggregate consumer prices; dynamic factor model; Phillips curve;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_593_20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.