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Statistical Graduation in Local Demographic Analysis and Projection

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  • Peter Congdon

Abstract

This paper considers parametric graduation for mortality, fertility and migration with particular reference to the development of parameterized local and regional demographic projections. Parametric graduations facilitate comparisons of demographic schedules across many areas and across time points—a feature which can be used to advantage in making forecasts of the three demographic components and thus in setting the assumptions for projections. Particular methodological issues raised are the questions of parsimony in fit and that of overdispersion in relation to binomial or Poisson assumptions. The analysis is illustrated with cross‐sectional material for the 32 London boroughs and with time series at the level of Greater London.

Suggested Citation

  • Peter Congdon, 1993. "Statistical Graduation in Local Demographic Analysis and Projection," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(2), pages 237-270, March.
    • Handle: RePEc:bla:jorssa:v:156:y:1993:i:2:p:237-270
    DOI: 10.2307/2982731
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    Cited by:

    1. Lucia Zanotto & Vladimir Canudas-Romo & Stefano Mazzuco, 2021. "A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 1-27, March.
    2. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    3. Hendrik Hansen & Peter Pflaumer, 2011. "Zur Prognose der Lebenserwartung in Deutschland: Ein Vergleich verschiedener Verfahren," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(3), pages 203-219, December.
    4. Njenga, Carolyn Ndigwako & Sherris, Michael, 2020. "Modeling mortality with a Bayesian vector autoregression," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 40-57.
    5. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    6. David Sharrow & Samuel J. Clark & Mark Collinson & Kathleen Kahn & Stephen Tollman, 2013. "The age pattern of increases in mortality affected by HIV," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(39), pages 1039-1096.
    7. Anastasia Kostaki & Vagelis Panousis, 2001. "Expanding an abridged life table," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 5(1), pages 1-22.
    8. Aude Bernard & Martin Bell, 2015. "Smoothing internal migration age profiles for comparative research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(33), pages 915-948.
    9. Alan Marshall & Paul Norman & Ian Plewis, 2013. "Applying Relational Models to the Graduation of Disability Schedules [Application de modèles relationnels pour le lissage de schémas d’incapacités]," European Journal of Population, Springer;European Association for Population Studies, vol. 29(4), pages 467-491, November.
    10. Ugofilippo Basellini & Vladimir Canudas-Romo & Adam Lenart, 2019. "Location–Scale Models in Demography: A Useful Re-parameterization of Mortality Models," European Journal of Population, Springer;European Association for Population Studies, vol. 35(4), pages 645-673, October.
    11. Luca Bagnato & Antonio Punzo, 2013. "Finite mixtures of unimodal beta and gamma densities and the $$k$$ -bumps algorithm," Computational Statistics, Springer, vol. 28(4), pages 1571-1597, August.

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