Subnational population forecasts: Do users want to know about uncertainty?

Wilson, Tom; Shalley, Fiona

doi:10.4054/demres.2019.41.13

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…It is not due to a lack of interest from users. In a recent survey, the majority of subnational population forecast users stated that they would like to receive information about population forecast uncertainty (Wilson & Shalley, 2019 ). Among the methods which quantify uncertainty, Tayman ( 2011 ) makes a broad distinction between empirically based prediction intervals based on analyses of historical forecast errors, and model-based intervals generated by probabilistic and statistical models.…”

Section: Small Area Population Forecasting Methods 2001–2020mentioning

confidence: 99%

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Wilson

Grossman

Alexander

et al. 2021

Popul Res Policy Rev

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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. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

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Section: Small Area Population Forecasting Methods 2001–2020mentioning

confidence: 99%

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Wilson

Grossman

Alexander

et al. 2021

Popul Res Policy Rev

Self Cite

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show abstract

“…Although other error thresholds could have been selected, 5% was chosen in this case because previous focus group research found that 3 to 5% error was regarded as acceptable by forecast users (Wilson and Shalley 2019). Alternative error thresholds could be chosen if desired.…”

Section: Shelf Life Estimationmentioning

confidence: 99%

Visualizing the Shelf Life of Population Forecasts: A Simple Approach to Communicating Forecast Uncertainty

Wilson

2024

Journal of Official Statistics

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It is widely appreciated that population forecasts are inherently uncertain. Researchers have responded by quantifying uncertainty using probabilistic forecasting methods. Yet despite several decades of development, probabilistic forecasts have gained little traction outside the academic sector. Therefore, this article suggests an alternative and simpler approach to estimating and communicating uncertainty which might be helpful for population forecast practitioners and users. Drawing on the naïve forecasts idea of Alho, it suggests creating “synthetic historical forecast errors” by running a regular deterministic projection model many times over recent decades. Then, borrowing from perishable food terminology, the “shelf life” of forecast variables, the number of years into the future the forecast is likely to remain “safe for consumption” (within a specified error tolerance), is estimated from the “historical” errors. The shelf lives are then applied to a current set of forecasts and presented in a simple manner in graphs and tables of forecasts using color-coding. The approach is illustrated through a case study of 2021-based population forecasts for Australia. It´s concluded that the approach offers a relatively straightforward way of estimating and communicating population forecast uncertainty.

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“…To evaluate the number of bad forecasts by our models we define the Percentage of Bad Forecasts (Wilson et al 2021b) as the percentage of forecasts which have an Absolute Percentage Error greater than 10% after 5 years, or greater than 20% after 10 years. These values were selected because they are larger than the levels of error acceptable to population forecast users surveyed by Wilson and Shalley (2019).…”

Section: Error Metricsmentioning

confidence: 99%

Forecasting small area populations with Long Short-Term Memory Networks

Grossman¹,

Wilson²,

Temple³

2022

Preprint

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Local and state governments depend on small area population forecasts to make important decisions concerning the development of local infrastructure and services, including schooling, transportation, healthcare, energy, telecommunications, and water supply. Despite their importance, current methods often produce highly inaccurate forecasts, especially at the small area scale. Recent years have witnessed promising developments in time series forecasting using Machine Learning across a wide range of social and economic variables. However, almost no work has been undertaken to investigate their potential application in demography, particularly for small area population forecasting. In this paper we describe the development of two Long-Short Term Memory network architectures for work with small area populations. We employ the Keras Tuner to select layer unit numbers, vary the window width of input data, and apply a double training and validation regime which supports work with short time series and prioritises later sequence values for forecasts. These methods are transferable and can be applied to other data sets. Retrospective small area population forecasts for Australia were created for the periods 2006-16 and 2011-16. Model performance was evaluated against actual data and two benchmark methods. The deep learning methods produced forecast errors comparable to the benchmark methods, markedly improving the 2006-based forecasts, but not 2011-based forecasts produced by one of the benchmark methods.

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Subnational population forecasts: Do users want to know about uncertainty?

Cited by 14 publications

References 27 publications

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Visualizing the Shelf Life of Population Forecasts: A Simple Approach to Communicating Forecast Uncertainty

Forecasting small area populations with Long Short-Term Memory Networks

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