Estimation of local demographic variation in a flexible framework for population projections

Simpson, Ludi; Snowling, Harvey

doi:10.1007/s12546-011-9060-7

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Smaller areas than the local authorities considered in this paper are also forecast in Britain. Although small area forecasts are not regular or official, they have a longstanding place within service planning and neighbourhood development (Rhodes and Naccache 1977;Simpson and Snowling 2011;NRS 2016), and a thorough evaluation is likely to be fruitful.…”

Section: Discussionmentioning

confidence: 99%

The Shelf Life of Official Sub-National Population Forecasts in England

Simpson

Wilson

Shalley

2019

Appl. Spatial Analysis

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We measure the empirical distribution of the accuracy of projected population in subnational areas of England, developing the concept of 'shelf life': the furthest horizon for which the subsequent best estimate of population is within 10% of the forecast, for at least 80% of areas projected. Since local government reorganisation in 1974, the official statistics agency has projected the population of each local government area in England: for 108 areas in nine forecasts up to the 1993-based, and for over 300 areas in 10 forecasts from the 1996-based to the 2014-based forecasts. By comparing the published forecast (we use this term rather than projection) with the post-census population estimates, the empirical distribution of errors has been described. It is particularly dependent on the forecast horizon and the type of local authority. For 10year forecasts the median absolute percentage error has been 7% for London Boroughs and 3% for Shire Districts. Users of forecasts tend to have in mind a horizon and a required accuracy that is of relevance to their application. A shelf life of 10 years is not sufficient if the user required that accuracy of a forecast 15 years ahead. The relevant effective shelf life deducts the user's horizon. We explore the empirical performance of official sub-national forecasts in this light. A five-year forecast for London Boroughs requiring 10% accuracy is already beyond its effective shelf life by the time it is published. Collaboration between forecasters and users of forecasts can develop information on uncertainty that is useful to planning.

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Section: Discussionmentioning

confidence: 99%

The Shelf Life of Official Sub-National Population Forecasts in England

Simpson

Wilson

Shalley

2019

Appl. Spatial Analysis

Self Cite

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show abstract

“…Dyrting ( 2020 ) extended this method using a penalised-splines approach, where the smoothness of the fit is controlled with a single parameter, and then applied the method to estimate small area age-specific migration schedules in the Northern Territory, Australia (Dyrting, 2020 ). Simpson and Snowling ( 2011 ) evaluated three methods for preparing input data for small area cohort-component forecasts where input data were not available (or affordable). Methods evaluated included a No Local Variation approach which uses the same fertility, mortality, and migration inputs for all small areas within a wider region.…”

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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“…Example forecasts were produced by the POPGROUP software, which incorporates the adjustment mechanisms. Simpson, L., & Snowling, H. (2011). Estimation of local demographic variation in a flexible framework for population projections.…”

Section: Population Research and Policymentioning

confidence: 99%

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

Wilson¹,

Grossman²,

Alexander³

et al. 2021

Preprint

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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 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.

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Estimation of local demographic variation in a flexible framework for population projections

Cited by 8 publications

References 9 publications

The Shelf Life of Official Sub-National Population Forecasts in England

The Shelf Life of Official Sub-National Population Forecasts in England

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

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