IDEAS home Printed from https://ideas.repec.org/a/fip/fedcec/y2013inov15n2013-16.html
   My bibliography  Save this article

Improving inflation forecasts in the medium to long term

Author

Listed:
  • Saeed Zaman

Abstract

To accurately forecast the future rate of inflation, it is imperative to account for inflation?s underlying trend. This is especially important for medium- to long-run forecasts. In this Commentary I demonstrate a simple but powerful technique for incorporating this trend into standard statistical time series models and report the gains to accuracy. I find that incorporating the trend by modeling inflation as gap from an estimated underlying trend leads to substantial gains in forecast accuracy of about 20 percent to 30 percent, two to three years out.

Suggested Citation

  • Saeed Zaman, 2013. "Improving inflation forecasts in the medium to long term," Economic Commentary, Federal Reserve Bank of Cleveland, issue Nov.
  • Handle: RePEc:fip:fedcec:y:2013:i:nov15:n:2013-16
    DOI: 10.26509/frbc-ec-201316
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-ec-201316
    File Function: Full Text
    Download Restriction: no

    File URL: https://libkey.io/10.26509/frbc-ec-201316?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Todd E. Clark, 2011. "Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 327-341, July.
    2. Kenneth Beauchemin, 2011. "Shocks and the economic outlook," Economic Commentary, Federal Reserve Bank of Cleveland, issue June.
    3. Kozicki, Sharon & Tinsley, P. A., 2001. "Term structure views of monetary policy under alternative models of agent expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 25(1-2), pages 149-184, 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. Elena Bobeica & Matteo Ciccarelli & Isabel Vansteenkiste, 2019. "The link between labor cost and price inflation in the euro area," Working Papers Central Bank of Chile 848, Central Bank of Chile.
    2. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    3. 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.
    4. 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.
    5. Bobeica, Elena & Ciccarelli, Matteo & Vansteenkiste, Isabel, 2021. "The changing link between labor cost and price inflation in the United States," Working Paper Series 2583, European Central Bank.
    6. Richard Ashley & Randal J. Verbrugge, 2019. "The Intermittent Phillips Curve: Finding a Stable (But Persistence-Dependent) Phillips Curve Model Specification," Working Papers 19-09R2, Federal Reserve Bank of Cleveland, revised 14 Feb 2023.
    7. Todd E. Clark & Edward S. Knotek & Saeed Zaman, 2015. "Measuring Inflation Forecast Uncertainty," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2015(03), pages 1-6, March.
    8. Edward S. Knotek & Saeed Zaman, 2013. "When Might the Federal Funds Rate Lift Off? Computing the Probabilities of Crossing Unemployment and Inflation Thresholds," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    9. Verbrugge, Randal & Zaman, Saeed, 2024. "Improving inflation forecasts using robust measures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 735-745.
    10. Edward S. Knotek & Saeed Zaman, 2014. "On the Relationships between Wages, Prices, and Economic Activity," Economic Commentary, Federal Reserve Bank of Cleveland, issue Aug.

    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. Edward S. Knotek & Saeed Zaman, 2013. "When Might the Federal Funds Rate Lift Off? Computing the Probabilities of Crossing Unemployment and Inflation Thresholds," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    2. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    3. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    4. Todd E. Clark & Saeed Zaman, 2013. "Forecasting implications of the recent decline in inflation," Economic Commentary, Federal Reserve Bank of Cleveland, issue Nov.
    5. Orphanides, Athanasios & Wei, Min, 2012. "Evolving macroeconomic perceptions and the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 239-254.
    6. Florian Huber & Tamás Krisztin & Philipp Piribauer, 2017. "Forecasting Global Equity Indices Using Large Bayesian Vars," Bulletin of Economic Research, Wiley Blackwell, vol. 69(3), pages 288-308, July.
    7. Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright, 2014. "Forecasting interest rates with shifting endpoints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 693-712, August.
    8. Sharon Kozicki & Peter A. Tinsley, 2002. "Alternative sources of the lag dynamics of inflation," Research Working Paper RWP 02-12, Federal Reserve Bank of Kansas City.
    9. Athanasios Orphanides & John C. Williams, 2007. "Inflation targeting under imperfect knowledge," Economic Review, Federal Reserve Bank of San Francisco, pages 1-23.
    10. Bulkley, George & Giordani, Paolo, 2011. "Structural breaks, parameter uncertainty, and term structure puzzles," Journal of Financial Economics, Elsevier, vol. 102(1), pages 222-232, October.
    11. Marcellino, Massimiliano & Carriero, Andrea & Tornese, Tommaso, 2022. "Blended Identification in Structural VARs," CEPR Discussion Papers 17640, C.E.P.R. Discussion Papers.
    12. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang, 2022. "Predicting returns and dividend growth — The role of non-Gaussian innovations," Finance Research Letters, Elsevier, vol. 46(PA).
    13. Peter Hooper & Frederic S. Mishkin & Amir Sufi, 2019. "Prospects for Inflation in a High Pressure Economy: Is the Phillips Curve Dead or is It Just Hibernating?," NBER Working Papers 25792, National Bureau of Economic Research, Inc.
    14. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    15. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    16. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019. "How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 229-248, Emerald Group Publishing Limited.
    17. Christopher Gust & Edward Herbst & David López-Salido, 2022. "Short-Term Planning, Monetary Policy, and Macroeconomic Persistence," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 174-209, October.
    18. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    19. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    20. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.

    More about this item

    Keywords

    Inflation (Finance); Forecasting;

    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:fedcec:y:2013:i:nov15:n:2013-16. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.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.