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Bayesian Probabilistic Projections of Life Expectancy for All Countries

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Author Info

  • Adrian Raftery

    ()

  • Jennifer Chunn
  • Patrick Gerland
  • Hana Ševčíková
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    Abstract

    We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we conducted an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995 and using the estimated model to forecast for the subsequent 10 years. The 10-year predictions had a mean absolute error of about 1 year, about 40 % less than the current UN methodology. The probabilistic forecasts were calibrated in the sense that, for example, the 80 % prediction intervals contained the truth about 80 % of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free, publicly available R software packages called bayesLife and bayesDem are available to implement the method. Copyright The Author(s) 2013

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    File URL: http://hdl.handle.net/10.1007/s13524-012-0193-x
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    Bibliographic Info

    Article provided by Springer in its journal Demography.

    Volume (Year): 50 (2013)
    Issue (Month): 3 (June)
    Pages: 777-801

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    Handle: RePEc:spr:demogr:v:50:y:2013:i:3:p:777-801

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    Web page: http://www.springer.com/economics/journal/13524

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    Related research

    Keywords: Bayesian hierarchical model; Double logistic function; Lee-Carter model; Life expectancy at birth; Markov chain Monte Carlo;

    References

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    12. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer, vol. 48(3), pages 815-839, August.
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    16. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268.
    17. Czado, Claudia & Delwarde, Antoine & Denuit, Michel, 2005. "Bayesian Poisson log-bilinear mortality projections," Insurance: Mathematics and Economics, Elsevier, vol. 36(3), pages 260-284, June.
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    19. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
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    Cited by:
    1. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer, vol. 50(6), pages 2037-2051, December.
    2. Bailey Fosdick & Adrian Raftery, 2014. "Regional probabilistic fertility forecasting by modeling between-country correlations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(35), pages 1011-1034, April.
    3. Guy J. Abel & Jakub Bijak & Jonathan J. Forster & James Raymer & Peter W.F. Smith & Jackie S.T. Wong, 2013. "Integrating uncertainty in time series population forecasts: An illustration using a simple projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(43), pages 1187-1226, December.
    4. Adrian Raftery & Nevena Lalic & Patrick Gerland, 2014. "Joint probabilistic projection of female and male life expectancy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(27), pages 795-822, March.
    5. French, Declan, 2014. "International mortality modelling—An economic perspective," Economics Letters, Elsevier, vol. 122(2), pages 182-186.

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