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 socioeconomic 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.

小区域人口预测被政府和企业广泛用于各种规划、研究和政策目的，并经常影响重大投资决策。然而，相对于国家和大型次国家区域预测而言，小区域人口预测方法和技术的工具箱是适度的。在本文中，我们评估了小区域人口预测的现状，并提出了进一步研究的领域。本文回顾了 2001-2020 年期间发表的关于小区域人口预测方法的文献。审查涵盖的关键主题是外推和比较方法、简化的队列组成方法、模型平均和组合、结合社会经济变量和空间关系、“缩小规模”和分解方法，将人口与住房联系起来，估计和预测小区域组件输入数据，微观模拟，机器学习和预测不确定性。然后提出了一些进一步研究的途径，包括更多关于模型平均和组合的工作，针对当前模型无法处理的情况开发新的预测方法，量化不确定性，探索机器学习和空间统计等方法，为从业者创建用户友好的工具，并了解有关如何使用预测的更多信息。