IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v68y2015i8p1755-1758.html
   My bibliography  Save this article

Decomposition of time-series by level and change

Author

Listed:
  • Tessier, Thomas H.
  • Armstrong, J. Scott

Abstract

This article examines whether decomposing time series data into two parts – level and change – produces forecasts that are more accurate than those from forecasting the aggregate directly. Prior research found that, in general, decomposition reduced forecasting errors by 35%. An earlier study on decomposition into level and change found a forecast error reduction of 23%. The current study found that nowcasts consisting of a simple average of estimates from preliminary surveys and econometric models of the U.S. lodging market, improved the accuracy of final estimates of levels. Forecasts of change from an econometric model and the improved nowcasts reduced forecast errors by 29% when compared to direct forecasts of the aggregate. Forecasts of change from an extrapolation model and the improved nowcasts reduced forecast errors by 45%. On average then, the error reduction for this study was 37%.

Suggested Citation

  • Tessier, Thomas H. & Armstrong, J. Scott, 2015. "Decomposition of time-series by level and change," Journal of Business Research, Elsevier, vol. 68(8), pages 1755-1758.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1755-1758
    DOI: 10.1016/j.jbusres.2015.03.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296315001514
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2015.03.035?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 22(Fall), pages 3-12.
    2. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    3. J. Scott Armstrong & Michael C. Grohman, 1972. "A Comparative Study of Methods for Long-Range Market Forecasting," Management Science, INFORMS, vol. 19(2), pages 211-221, October.
    4. Victor Zarnowitz, 1967. "An Appraisal of Short-Term Economic Forecasts," NBER Books, National Bureau of Economic Research, Inc, number zarn67-1.
    5. McNees, Stephen K., 1990. "The role of judgment in macroeconomic forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 6(3), pages 287-299, October.
    6. Rosanne Cole, 1969. "Data Errors and Forecasting Accuracy," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 47-82, National Bureau of Economic Research, Inc.
    7. Armstrong, J. Scott, 1970. "An Application of Econometric Models to International Marketing," MPRA Paper 81698, University Library of Munich, Germany.
    8. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
    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. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    3. Goodwin, Paul, 2015. "When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts," Journal of Business Research, Elsevier, vol. 68(8), pages 1686-1691.
    4. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.

    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. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. repec:lan:wpaper:470 is not listed on IDEAS
    3. Didier Borowski & Carine Bouthevillain & Catherine Doz & Pierre Malgrange & Pierre Morin, 1991. "Vingt ans de prévisions macro-économiques : une évaluation sur données françaises," Économie et Prévision, Programme National Persée, vol. 99(3), pages 43-65.
    4. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    5. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    6. Andreas Graefe & Kesten C Green & J Scott Armstrong, 2019. "Accuracy gains from conservative forecasting: Tests using variations of 19 econometric models to predict 154 elections in 10 countries," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    7. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
    10. Andreas Graefe, 2018. "Predicting elections: Experts, polls, and fundamentals," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(4), pages 334-344, July.
    11. Dean Croushore & Tom Stark, 2002. "Is macroeconomic research robust to alternative data sets?," Working Papers 02-3, Federal Reserve Bank of Philadelphia.
    12. Dean Croushore & Tom Stark, 2000. "A real-time data set for macroeconomists: does data vintage matter for forecasting?," Working Papers 00-6, Federal Reserve Bank of Philadelphia.
    13. Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
    14. repec:lan:wpaper:425 is not listed on IDEAS
    15. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
    16. repec:lan:wpaper:539557 is not listed on IDEAS
    17. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
    18. repec:lan:wpaper:413 is not listed on IDEAS
    19. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    20. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    21. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    22. repec:cup:judgdm:v:13:y:2018:i:4:p:334-344 is not listed on IDEAS
    23. Karen E. Dynan & Douglas W. Elmendorf, 2001. "Do provisional estimates of output miss economic turning points?," Finance and Economics Discussion Series 2001-52, Board of Governors of the Federal Reserve System (U.S.).
    24. Frederick H. Wallace & Gary L. Shelley & Luis F. Cabrera Castellanos, 2004. "Pruebas de la neutralidad monetaria a largo plazo: el caso de Nicaragua," Monetaria, CEMLA, vol. 0(4), pages 407-418, octubre-d.
    25. Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.

    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:eee:jbrese:v:68:y:2015:i:8:p:1755-1758. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.