IDEAS home Printed from https://ideas.repec.org/a/fip/fedlrv/y1997imayp143-155.html
   My bibliography  Save this article

Measuring short-run inflation for central bankers

Author

Listed:
  • Stephen G. Cecchetti

Abstract

As central bankers intensify their focus on inflation as the primary goal of monetary policy, it becomes increasingly important to have accurate and reliable measures of changes in the aggregate price level. Measuring inflation is surprisingly difficult, involving two types of problems. Commonly used indices, such as the Consumer Price Index (CPI), contain both transitory noise and bias. Noise causes short-run changes in measured inflation to inaccurately reflect movements in long-run trends, while bias leads the long-run average change in the CPI to be too high. In this paper I propose methods of reducing both the noise and the bias in the CPI. Noise reduction is achieved by average monthly inflation in measures called trimmed means' over longer horizons. Trimmed means are statistics similar to the median that are calculated by ignoring the CPI components with extreme high and low changes each month, and averaging the rest. I find that using three month averages halves the noise, while removing the highest and lowest ten percent of the cross-sectional distribution of inflation reduces the monthly variation in inflation by one-fifth.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Stephen G. Cecchetti, 1997. "Measuring short-run inflation for central bankers," Review, Federal Reserve Bank of St. Louis, issue May, pages 143-155.
    • Handle: RePEc:fip:fedlrv:y:1997:i:may:p:143-155
    as

    Download full text from publisher

    File URL: https://files.stlouisfed.org/files/htdocs/publications/review/97/05/9705sc.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Wynne, Mark A & Sigalla, Fiona D, 1996. "A Survey of Measurement Biases in Price Indexes," Journal of Economic Surveys, Wiley Blackwell, vol. 10(1), pages 55-89, March.
    2. 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.
    3. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    4. Stephen G. Cecchetti & Anil K. Kashyap & David W. Wilcox, 1995. "Do Firms Smooth the Seasonal in Production in a Boom? Theory and Evidence," NBER Working Papers 5011, National Bureau of Economic Research, Inc.
    5. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    6. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    7. Laurence Ball & N. Gregory Mankiw, 1995. "Relative-Price Changes as Aggregate Supply Shocks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 161-193.
    8. Gordon, Robert J, 1992. "Measuring the Aggregate Price Level: Implications for Economic Performance and Policy," CEPR Discussion Papers 663, C.E.P.R. Discussion Papers.
    9. David E. Lebow & John M. Roberts & David J. Stockton, 1992. "Economic performance under price stability," Working Paper Series / Economic Activity Section 125, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Inoue, Tetsuya, 1998. "Impact of Information Technology and Implications for Monetary Policy," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 16(2), pages 29-60, December.
    3. Buono, Dario & Alpay, Kocak, 2010. "Backward recalculation of seasonal series affected by economic crisis: a Model-Based-Link method for the case of Turkish GDP," MPRA Paper 40092, University Library of Munich, Germany.
    4. Barnett, William A. & de Peretti, Philippe, 2009. "Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability," Macroeconomic Dynamics, Cambridge University Press, vol. 13(S2), pages 317-334, September.
    5. Wildi, Marc & McElroy, Tucker S., 2019. "The trilemma between accuracy, timeliness and smoothness in real-time signal extraction," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1072-1084.
    6. 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.
    7. Seamus Hogan & Marianne Johnson & Thérèse Laflèche, 2001. "Core Inflation," Technical Reports 89, Bank of Canada.
    8. Tommaso Proietti, 2012. "Seasonality, Forecast Extensions And Business Cycle Uncertainty," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 555-569, September.
    9. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    10. Kaiser, Regina & Maravall, Agustin, 2005. "Combining filter design with model-based filtering (with an application to business-cycle estimation)," International Journal of Forecasting, Elsevier, vol. 21(4), pages 691-710.
    11. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    12. Barend Abeln & Jan P. A. M. Jacobs, 2023. "CAMPLET: Seasonal Adjustment Without Revisions," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 7-29, Springer.
    13. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    14. Edward E. Leamer, 2011. "Workday, Holiday and Calendar Adjustment with 21st Century Data: Monthly Aggregates from Daily Diesel Fuel Purchases," NBER Working Papers 16897, National Bureau of Economic Research, Inc.
    15. Ghysels, E., 1993. "A Time Series Model with Periodic Stochastic Regime Switching," Cahiers de recherche 9314, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    16. Kubota, Keiichi & Tokunaga, Toshifumi & Wada, Kenji, 2008. "Consumption behavior, asset returns, and risk aversion: Evidence from the Japanese household survey," Japan and the World Economy, Elsevier, vol. 20(1), pages 1-18, January.
    17. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    18. Charles Steindel, 1997. "Are there good alternatives to the CPI?," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 3(Apr).
    19. Javier Escobal & Marco Castillo del Castillo, 1994. "Sesgos en la medición de la inflación en contextos inflacionarios: El caso peruano," Documentos de Investigación dt21, Grupo de Análisis para el Desarrollo (GRADE).
    20. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.

    More about this item

    Keywords

    Monetary policy; Inflation (Finance); Banks and banking; Central;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:fip:fedlrv:y:1997:i:may:p:143-155. 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: Scott St. Louis (email available below). General contact details of provider: https://edirc.repec.org/data/frbslus.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.