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Lee-Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections

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  • Arthur Renshaw
  • Steven Haberman
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    Abstract

    The paper presents a reinterpretation of the model underpinning the Lee-Carter methodology for forecasting mortality (and other vital) rates. A parallel methodology based on generalized linear modelling is introduced. The use of residual plots is proposed for both methods to aid the assessment of the goodness of fit. The two methods are compared in terms of structure and assumptions. They are then compared through an analysis of the gender- and age-specific mortality rates for England and Wales over the period 1950-1998 and through a consideration of the forecasts generated by the two methods. The paper also compares different approaches to the forecasting of life expectancy and considers the effectiveness of the Coale-Guo method for extrapolating mortality rates to the oldest ages. Copyright 2003 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series C (Applied Statistics).

    Volume (Year): 52 (2003)
    Issue (Month): 1 ()
    Pages: 119-137

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    Handle: RePEc:bla:jorssc:v:52:y:2003:i:1:p:119-137

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    Cited by:
    1. Mariarosaria Coppola & Valeria D'Amato, 2012. "Backtesting the solvency capital requirement for longevity risk," Journal of Risk Finance, Emerald Group Publishing, Emerald Group Publishing, vol. 13(4), pages 309-319.
    2. Han Lin Shang & Rob J Hyndman & Heather Booth, 2010. "A comparison of ten principal component methods for forecasting mortality rates," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 8/10, Monash University, Department of Econometrics and Business Statistics.
    3. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer, Springer, vol. 50(1), pages 261-283, February.
    4. Han Lin Shang, 2012. "Point and interval forecasts of age-specific life expectancies," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(21), pages 593-644, November.
    5. Markéta Arltová & Jitka Langhamrová & Jana Langhamrová, 2013. "Development of Life Expectancy in the Czech Republic in Years 1920-2010 with an Outlook to 2050," Prague Economic Papers, University of Economics, Prague, University of Economics, Prague, vol. 2013(1), pages 125-143.
    6. D’Amato, Valeria & Haberman, Steven & Piscopo, Gabriella & Russolillo, Maria, 2012. "Modelling dependent data for longevity projections," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 694-701.
    7. Ornelas, Arelly & Guillén, Montserrat, 2013. "A Comparison between General Population Mortality and Life Tables for Insurance in Mexico under Gender Proportion Inequality || Una comparación entre la mortalidad de la población general y las tabl," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative M, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 47-67, December.
    8. Jorge Bravo & Carlos Pereira da Silva, 2012. "Prospective Lifetables: Life Insurance Pricing and Hedging in a Stochastic Mortality Environment," CEFAGE-UE Working Papers 2012_01, University of Evora, CEFAGE-UE (Portugal).
    9. Heather Booth & Rob Hyndman & Leonie Tickle & Piet de Jong, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(9), pages 289-310, October.
    10. Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214, July.
    11. Kaishev, Vladimir K. & Dimitrova, Dimitrina S. & Haberman, Steven, 2007. "Modelling the joint distribution of competing risks survival times using copula functions," Insurance: Mathematics and Economics, Elsevier, vol. 41(3), pages 339-361, November.
    12. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(3), pages 547-581.

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