Point and interval forecasts of age-specific life expectancies: A model averaging approach

Han Lin Shang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Background: Any improvement in the forecast accuracy of life expectancy would be beneficial for policy decision regarding the allocation of current and future resources. In this paper, I revisit some methods for forecasting age-specific life expectancies.

Objective: This paper proposes a model averaging approach to produce accurate point forecasts of age-specific life expectancies.

Methods: Illustrated by data from fourteen developed countries, we compare point and interval forecasts among ten principal component methods, two random walk methods, and two univariate time-series methods.

Results: Based on averaged one-step-ahead and ten-step-ahead forecast errors, random walk with drift and Lee-Miller methods are the two most accurate methods for producing point forecasts. By combining their forecasts, point forecast accuracy is improved. As measured by averaged coverage probability deviance, the Hyndman-Ullah methods generally provide more accurate interval forecasts than the Lee-Carter methods. However, the Hyndman-Ullah methods produce wider half-widths of prediction interval than the Lee-Carter methods.

Conclusions: Model averaging approach should be considered to produce more accurate point forecasts.

Comments: This study is a sequel to another Demographic Research paper by Shang, Booth and Hyndman (2011), in which the authors compared the principal component methods for forecasting age-specific mortality rates and life expectancy at birth.
Original languageEnglish
Article number21
Pages (from-to)593–644
Number of pages52
JournalDemographic Research
Volume27
DOIs
Publication statusPublished - 9 Nov 2012
Externally publishedYes

Keywords

  • Booth-Maindonald-Smith method
  • functional data analysis
  • Hyndman-Ullah method
  • Lee-Carter model
  • Lee-Miller method
  • principal components analysis

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