IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbsps/202038.html
   My bibliography  Save this paper

PCCI – a data-rich measure of underlying inflation in the euro area

Author

Listed:
  • Bańbura, Marta
  • Bobeica, Elena

Abstract

This paper details the rationale and methodology behind the construction of the Persistent and Common Component of Inflation (PCCI), a measure of underlying inflation in the euro area. The PCCI reflects the view that underlying inflation captures widespread developments across the Harmonised Index of Consumer Prices (HICP) basket and that it is the persistent component of inflation. Methodologically, it relies on a generalised dynamic factor model estimated on a large set of disaggregated HICP inflation rates for 12 euro area countries. For each individual inflation rate, we estimate a low-frequency common component, i.e. a component driven by shocks or factors that are relevant for all inflation series and capturing cycles longer than three years. The PCCI is a weighted average of these common components. It is an alternative to the typical exclusion-based measures used to gauge underlying inflation (e.g. HICP excluding food and energy), as it does not a priori exclude any HICP items. It exhibits a set of desirable properties as a measure of underlying inflation, and it is a good tracker of more lasting inflationary developments (judging by smoothness and bias). Furthermore, it is timely and signals turning points with some lead, while acting as an attractor for headline inflation. JEL Classification: C32, E31, E32, E52

Suggested Citation

  • Bańbura, Marta & Bobeica, Elena, 2020. "PCCI – a data-rich measure of underlying inflation in the euro area," Statistics Paper Series 38, European Central Bank.
  • Handle: RePEc:ecb:ecbsps:202038
    Note: 810771
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecb.sps38~ce391a0cb5.en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Ehrmann, Michael & Ferrucci, Gianluigi & Lenza, Michele & O'Brien, Derry, 2018. "Measures of underlying inflation for the euro area," Economic Bulletin Articles, European Central Bank, vol. 4.
    4. Todd E. Clark, 2001. "Comparing measures of core inflation," Economic Review, Federal Reserve Bank of Kansas City, vol. 86(Q II), pages 5-31.
    5. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    6. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    7. Michael F. Bryan & Stephen G. Cecchetti, 1993. "The consumer price index as a measure of inflation," Economic Review, Federal Reserve Bank of Cleveland, vol. 29(Q IV), pages 15-24.
    8. 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.
    9. José Ferreira Machado, 2001. "Using the First Principal Component as a Core Inflation Indicator," Working Papers w200109, Banco de Portugal, Economics and Research Department.
    10. Marlene Amstad & Simon M. Potter & Robert W. Rich, 2014. "The FRBNY staff underlying inflation gauge: UIG," Staff Reports 672, Federal Reserve Bank of New York.
    11. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    12. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    13. Mark A. Wynne, 2008. "Core inflation: a review of some conceptual issues," Review, Federal Reserve Bank of St. Louis, vol. 90(May), pages 205-228.
    14. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    15. Andersson, Malin & Szörfi, Béla & Tóth, Máté & Zorell, Nico, 2018. "Potential output in the post-crisis period," Economic Bulletin Articles, European Central Bank, vol. 7.
    16. Elmar Mertens, 2016. "Measuring the Level and Uncertainty of Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 950-967, December.
    17. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    18. Carlos Robalo Marques & Pedro Duarte Neves & Afonso Gonçalves da Silva, 2001. "Using the first principal component as a core inflation indicator," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    19. Robert W. Rich & Charles Steindel, 2007. "A comparison of measures of core inflation," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Dec), pages 19-38.
    20. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    21. Michael Kirker, 2010. "What drives core inflation? A dynamic factor model analysis of tradable and nontradable prices," Reserve Bank of New Zealand Discussion Paper Series DP2010/13, Reserve Bank of New Zealand.
    22. Michal Andrle & Jan Bruha & Serhat Solmaz, 2013. "Inflation and Output Comovement in the Euro Area: Love at Second Sight?," Working Papers 2013/07, Czech National Bank.
    23. M. Henry Linder & Richard Peach & Robert W. Rich, 2013. "The parts are more than the whole: separating goods and services to predict core inflation," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 19(Aug).
    24. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, March.
    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. Bobeica Elena & Holton Sarah & Koester Gerrit, 2023. "Bringing Inflation Back Under Control," Intereconomics: Review of European Economic Policy, Sciendo, vol. 58(3), pages 136-141, June.
    2. Ampudia, Miguel & Ehrmann, Michael & Strasser, Georg, 2023. "The effect of monetary policy on inflation heterogeneity along the income distribution," Working Paper Series 2858, European Central Bank.
    3. Le Bihan, Hervé & Leiva-Leon, Danilo & Pacce, Matías, 2023. "Underlying inflation and asymmetric risks," Working Paper Series 2848, European Central Bank.
    4. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.

    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. Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
    2. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    3. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    4. Juan Angel Garcia & Aubrey Poon, 2022. "Inflation trends in Asia: implications for central banks [Are Phillips curves useful for forecasting inflation?]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 671-700.
    5. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Annual Conference Volume (Discontinued), in: Christopher Kent & David Norman (ed.),The Changing Nature of the Business Cycle, Reserve Bank of Australia.
    6. Le Bihan, Hervé & Leiva-Leon, Danilo & Pacce, Matías, 2023. "Underlying inflation and asymmetric risks," Working Paper Series 2848, European Central Bank.
    7. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
    8. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    9. Bańbura, Marta & Leiva-León, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Discussion Papers 48/2021, Deutsche Bundesbank.
    10. 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.
    11. Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2013. "Changes in the effects of monetary policy on disaggregate price dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 543-560.
    12. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2006. "Are European business cycles close enough to be just one?," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1687-1706.
    13. Igan, Deniz & Kabundi, Alain & Nadal De Simone, Francisco & Pinheiro, Marcelo & Tamirisa, Natalia, 2011. "Housing, credit, and real activity cycles: Characteristics and comovement," Journal of Housing Economics, Elsevier, vol. 20(3), pages 210-231, September.
    14. 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.
    15. Maria Gadea & Ana Gómez-Loscos & Antonio Montañés, 2012. "Cycles inside cycles: Spanish regional aggregation," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 423-456, December.
    16. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    17. Barigozzi, Matteo & Hallin, Marc & Luciani, Matteo & Zaffaroni, Paolo, 2024. "Inferential theory for generalized dynamic factor models," Journal of Econometrics, Elsevier, vol. 239(2).
    18. Domenico Giannone & Troy D. Matheson, 2007. "A New Core Inflation Indicator for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 145-180, December.
    19. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," DSS Empirical Economics and Econometrics Working Papers Series 2011/5, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    20. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.

    More about this item

    Keywords

    dynamic factor model; frequency domain; Underlying (core) inflation;
    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:ecb:ecbsps:202038. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.