Bayesian Population Forecasting: Extending the Lee-Carter Method.

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a...

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Publicado en:Demography (Springer Nature) Vol. 52; no. 3; pp. 1035 - 1060
Autores principales: Wiśniowski, Arkadiusz, Smith, Peter, Bijak, Jakub, Raymer, James, Forster, Jonathan
Formato: Artículo
Publicado: Springer Nature Jun2015
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Bayesian Population Forecasting: Extending the Lee-Carter Method.
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        au:
          Wiśniowski, Arkadiusz
          Smith, Peter
          Bijak, Jakub
          Raymer, James
          Forster, Jonathan
        affil:
          Economic and Social Research Council Centre for Population Change, University of Southampton, Highfield SO17 1BJ Southampton UK
          Australian Demographic & Social Research Institute, The Australian National University, Acton ACT 2601 Australia
      su:
        Population forecasting
        Social prediction
        Population statistics
        Bayesian analysis
        Bayes' estimation
      sug:
        subj:
          Population forecasting
          Social prediction
          Population statistics
          Bayesian analysis
          Bayes' estimation
      keyword:
        Bayesian
        Lee-Carter model
        uncertainty
        United Kingdom
        Bayesian
        Lee-Carter model
        uncertainty
        United Kingdom
      ab: In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.
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    language: English
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