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Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections

Author

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  • James Raymer

    (ESRC Research Centre for Population Change, University of Southampton, Southampton SO17 1BJ, England)

  • Guy J Abel

    (Wittgenstein Centre for Demography and Global Human Capital and Vienna Institute of Demography, Austrian Academy of Sciences, 1040 Vienna, Austria)

  • Andrei Rogers

    (Population Program, Institute of Behavioral Science, University of Colorado, Boulder, CO 80303, USA)

Abstract

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands, and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends with a discussion of our results and possible directions for future research.

Suggested Citation

  • James Raymer & Guy J Abel & Andrei Rogers, 2012. "Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections," Environment and Planning A, , vol. 44(11), pages 2664-2686, November.
    • Handle: RePEc:sae:envira:v:44:y:2012:i:11:p:2664-2686
    DOI: 10.1068/a4533
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    References listed on IDEAS

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    1. Joao Saboia, 1974. "Modeling and forecasting populations by time series: The Swedish case," Demography, Springer;Population Association of America (PAA), vol. 11(3), pages 483-492, August.
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    Cited by:

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    2. Cornelius Köpp & Hans-Jörg Mettenheim & Michael Breitner, 2014. "Decision Analytics with Heatmap Visualization for Multi-step Ensemble Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 131-140, June.
    3. Jie Chen & Yujie Liu & Ermei Zhang & Tao Pan & Yanhua Liu, 2022. "Estimating China’s Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    4. Shang, Han Lin & Smith, Peter W.F. & Bijak, Jakub & Wiśniowski, Arkadiusz, 2016. "A multilevel functional data method for forecasting population, with an application to the United Kingdom," International Journal of Forecasting, Elsevier, vol. 32(3), pages 629-649.
    5. Hana Sevcikova & Adrian E. Raftery & Patrick Gerland, 2018. "Probabilistic projection of subnational total fertility rates," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(60), pages 1843-1884.

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