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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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    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.
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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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