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Methods for small area population forecasts: state-of-the-art and research needs

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  • Wilson, Thomas

    (The University of Melbourne)

  • Grossman, Irina
  • Alexander, Monica
  • Rees, Philip
  • Temple, Jeromey

Abstract

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

Suggested Citation

  • 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.
  • Handle: RePEc:osf:socarx:sp6me
    DOI: 10.31219/osf.io/sp6me
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    References listed on IDEAS

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    1. Georgios Xanthos & Christos Ap. Ladias & Christos Genitsaropoulos, 2013. "A Method For Forecasting Population Changes In Alpine, Semi-Alpine And Lowland Communities Of Epirus Region In Greece," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 173-179, June.
    2. Hamidreza Zoraghein & Brian C. O'Neill, 2020. "A spatial population downscaling model for integrated human-environment analysis in the United States," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(54), pages 1563-1606.
    3. Hamidreza Zoraghein & Brian C. O’Neill, 2020. "U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways," Sustainability, MDPI, vol. 12(8), pages 1-26, April.
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    Cited by:

    1. 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.
    2. Sebal Oo & Makoto Tsukai, 2022. "Long-Term Impact of Interregional Migrants on Population Prediction," Sustainability, MDPI, vol. 14(11), pages 1-21, May.
    3. Tom Wilson, 2022. "Preparing local area population forecasts using a bi-regional cohort-component model without the need for local migration data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(32), pages 919-956.
    4. Grossman, Irina & Wilson, Tom & Temple, Jeromey, 2023. "Forecasting small area populations with long short-term memory networks," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

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