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Evaluation of the best M4 competition methods for small area population forecasting

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  • Wilson, Tom
  • Grossman, Irina
  • Temple, Jeromey

Abstract

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.

Suggested Citation

  • Wilson, Tom & Grossman, Irina & Temple, Jeromey, 2023. "Evaluation of the best M4 competition methods for small area population forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 110-122.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:1:p:110-122
    DOI: 10.1016/j.ijforecast.2021.09.005
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    References listed on IDEAS

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    1. Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.
    2. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    3. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    4. Mario Reinhold & Stephan Thomsen, 2015. "Subnational Population Projections by Age: An Evaluation of Combined Forecast Techniques," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(4), pages 593-613, August.
    5. Petropoulos, Fotios & Svetunkov, Ivan, 2020. "A simple combination of univariate models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 110-115.
    6. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
    7. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    8. Panagiotelis, Anastasios & Athanasopoulos, George & Gamakumara, Puwasala & Hyndman, Rob J., 2021. "Forecast reconciliation: A geometric view with new insights on bias correction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 343-359.
    9. Wilson, Thomas & Grossman, Irina & Alexander, Monica & Rees, Philip & Temple, Jeromey, 2021. "Methods for small area population forecasts: state-of-the-art and research needs," SocArXiv sp6me, Center for Open Science.
    10. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    11. Jeff Tayman, 2011. "Assessing Uncertainty in Small Area Forecasts: State of the Practice and Implementation Strategy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(5), pages 781-800, October.
    12. Tom Wilson & Huw Brokensha & Francisco Rowe & Ludi Simpson, 2018. "Insights from the Evaluation of Past Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 137-155, February.
    13. Fiorucci, Jose Augusto & Louzada, Francisco, 2020. "GROEC: Combination method via Generalized Rolling Origin Evaluation," International Journal of Forecasting, Elsevier, vol. 36(1), pages 105-109.
    14. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    15. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    16. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    17. Tom Wilson, 2016. "Evaluation of Alternative Cohort-Component Models for Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(2), pages 241-261, April.
    18. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
    19. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2020. "Card forecasts for M4," International Journal of Forecasting, Elsevier, vol. 36(1), pages 129-134.
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    More about this item

    Keywords

    Population forecast; Forecast error; Small area; M4 competition; Australia; New Zealand;
    All these keywords.

    JEL classification:

    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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