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Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be...

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Detalles Bibliográficos
Autores principales: Rayer, Stefan, Smith, Stanley K., Tayman, Jeff
Formato: Texto
Lenguaje:English
Publicado: Springer Netherlands 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2778678/
https://www.ncbi.nlm.nih.gov/pubmed/19936030
http://dx.doi.org/10.1007/s11113-009-9128-7
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author Rayer, Stefan
Smith, Stanley K.
Tayman, Jeff
author_facet Rayer, Stefan
Smith, Stanley K.
Tayman, Jeff
author_sort Rayer, Stefan
collection PubMed
description Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.
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spelling pubmed-27786782009-11-20 Empirical Prediction Intervals for County Population Forecasts Rayer, Stefan Smith, Stanley K. Tayman, Jeff Popul Res Policy Rev Article Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future. Springer Netherlands 2009-02-10 2009 /pmc/articles/PMC2778678/ /pubmed/19936030 http://dx.doi.org/10.1007/s11113-009-9128-7 Text en © The Author(s) 2009 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Rayer, Stefan
Smith, Stanley K.
Tayman, Jeff
Empirical Prediction Intervals for County Population Forecasts
title Empirical Prediction Intervals for County Population Forecasts
title_full Empirical Prediction Intervals for County Population Forecasts
title_fullStr Empirical Prediction Intervals for County Population Forecasts
title_full_unstemmed Empirical Prediction Intervals for County Population Forecasts
title_short Empirical Prediction Intervals for County Population Forecasts
title_sort empirical prediction intervals for county population forecasts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2778678/
https://www.ncbi.nlm.nih.gov/pubmed/19936030
http://dx.doi.org/10.1007/s11113-009-9128-7
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