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A comparison of ten principal component methods for forecasting mortality rates

By Han Lin Shang, Rob J Hyndman and Heather Booth


Using the age- and sex-specific data of 14 developed countries, we compare the short- to medium-term accuracy of ten principal component methods for forecasting mortality rates and life expectancy. These ten methods include the Lee-Carter method and many of its variants and extensions. For forecasting mortality rates, the weighted Hyndman-Ullah method provides the most accurate point forecasts, while the Lee-Miller method gives the best point forecast accuracy of life expectancy. Furthermore, the weighted Hyndman-Ullah method provides the most accurate interval forecasts of mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy of life expectancy.Mortality forecasting, life expectancy forecasting, principal component methods, Lee-Carter method, interval forecasts, forecasting time series

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  1. (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors’,
  2. (2006). A comparison of models for dynamic life tables. Application to mortality data from the Valencia Region (Spain)’, Lifetime Data Analysis
  3. (2000). A discussion of parameter and model uncertainty in insurance’,
  4. (2009). A multivariate time series approach to projected life tables’,
  5. (2002). A Poission log-bilinear regression approach to the construction of projected lifetables’,
  6. (1998). A stochastic forecast of the population of Finland, Reviews 1998/4, Statistics Finland,
  7. (2000). A universal pattern of mortality decline in the G7 countries’,
  8. (2002). Applying Lee-Carter under conditions of variable mortality decline’,
  9. (2008). Automatic time series forecasting: the forecast package for R’,
  10. (2005). Bootstrapping the Poisson log-bilinear model for mortality forecasting’,
  11. (2002). Broken limits to life expectancy’,
  12. (1995). Changing kinship structure and its implications for old-age support in urban and rural China’,
  13. (2008). Demographic Forecasting,
  14. (2006). Demographic forecasting: 1980 to 2005 in review’,
  15. (2009). demography: Forecasting mortality, fertility, migration and population data.
  16. (2006). Evaluating and extending the Lee-Carter model for mortality forecasting: bootstrap confidence interval’,
  17. (2005). Evaluation of the variants of the lee-carter method of forecasting mortality: a multi-country comparison’,
  18. (2001). Examining structural shifts in mortality using the LeeCarter method, Working paper 2001-007, Max Planck Institute for Demographic Research. URL: Chatfield,
  19. (2006). Extending Lee-Carter mortality forecasting’,
  20. (2008). Extrapolative projections of mortality: towards a more consistent method, Working paper 3/2008, Vienna Institute of Demography. URL: Erbas,
  21. (2009). Forecasting functional time series (with discussion)’,
  22. (2008). Forecasting with Exponential Smoothing: the State Space Approach,
  23. (2005). Functional Data Analysis, 2nd edn,
  24. (2004). Introduction: how to deal with uncertainty in population forecasting?’,
  25. (2003). Lee-Carter mortality forecasting with age-specific enhancement’,
  26. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions’,
  27. (2003). Lee-Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections’,
  28. (2005). Long-range trends in adult mortality: models and projection methods’,
  29. (2002). Longevity advances in high-income countries, 1955-96’, Population and Development Review
  30. (1993). Modeling and forecasting provincial mortality in Canada,
  31. (1992). Modeling and forecasting U.S. mortality’,
  32. (1994). Modeling and projecting mortality in Chile’,
  33. (1988). Monotone regression splines in action’,
  34. (2004). Mortality forecasting and trend shifts: an application of the Lee-Carter model to Swedish mortality data’,
  35. (2008). Mortality modelling and forecasting: a review of methods’,
  36. (1996). Mortality projections for Japan: a comparison of four methods,
  37. (2008). Mortality, longevity and experiments with the Lee-Carter model’,
  38. (2008). On simulation-based approaches to risk measurement in mortality with specific reference to Poisson Lee-Carter modelling’,
  39. (2003). On the forecasting of mortality reduction factors’,
  40. (2007). Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models’,
  41. (2008). R package version 1.09-1. URL:
  42. (2002). Recent mortality trends in the Spanish population’,
  43. (2007). Robust forecasting of mortality and fertility rates: a functional data approach’,
  44. (1997). Scenarios, uncertainty and conditional forecasts of the world population’,
  45. (2004). Smoothing and forecasting mortality rates’,
  46. (2003). Spline estimators for the functional linear model’,
  47. (2005). Statistical Demography and Forecasting,
  48. (2005). Stochastic forecasts of mortality, population, and pension systems,
  49. (2003). The need for looking far back in time when predicting future mortality trends,
  50. (1998). The role of population size in the determination and prediction of population forecast errors: an evaluation using confidence intervals for subcounty areas’,
  51. (2000). Time-Series Forecasting, Chapman & Hall/CRC,
  52. (2002). Why population forecasts should be probabilistic— illustrated by the case of Norway’,

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