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Point and interval forecasts of age-specific life expectancies

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  • Han Lin Shang

    (Australian National University)

Abstract

This paper considers forecasting life table, and proposes a model averaging approach to improve point and interval forecast accuracy. Illustrated by data of eleven countries, we compare point and interval forecasts among ten principal component and two random walk methods. Based on averaged forecast errors, random walk with drift and Lee-Miller methods are two most accurate methods for producing point forecasts. By combining their forecasts, point forecast accuracy is improved. As measured by averaged coverage probability deviance, Hyndman-Ullah methods provide more accurate interval forecasts than Lee-Carter methods. By combining forecasts from two most accurate methods, interval forecast accuracy is improved.

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File URL: http://www.demographic-research.org/volumes/vol27/21/27-21.pdf
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Bibliographic Info

Article provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.

Volume (Year): 27 (2012)
Issue (Month): 21 (November)
Pages: 593-644

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Handle: RePEc:dem:demres:v:27:y:2012:i:21

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Web page: http://www.demogr.mpg.de/

Related research

Keywords: Booth-Maindonald-Smith method; functional data analysis; Hyndman-Ullah method; Lee-Carter method; Lee-Miller method; principal components analysis; random walk with drift;

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  1. Rob J Hyndman & Heather Booth, 2006. "Stochastic population forecasts using functional data models for mortality, fertility and migration," Monash Econometrics and Business Statistics Working Papers 14/06, Monash University, Department of Econometrics and Business Statistics.
  2. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
  3. 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.
  4. 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.
  5. Arthur Renshaw & Steven Haberman, 2003. "Lee-Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 119-137.
  6. Rob J. Hyndman & Md. Shahid Ullah, 2005. "Robust forecasting of mortality and fertility rates: a functional data approach," Monash Econometrics and Business Statistics Working Papers 2/05, Monash University, Department of Econometrics and Business Statistics.
  7. Alho, Juha M., 1990. "Stochastic methods in population forecasting," International Journal of Forecasting, Elsevier, vol. 6(4), pages 521-530, December.
  8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
  9. Rob J. Hyndman & Yeasmin Khandakar, . "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, American Statistical Association, vol. 27(i03).
  10. Rowland, Donald T., 2003. "Demographic Methods and Concepts," OUP Catalogue, Oxford University Press, number 9780198752639.
  11. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer, vol. 38(4), pages 537-549, November.
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