Volume 27 - Article 21 | Pages 593-644

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

By Han Lin Shang

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Date received:30 Sep 2011
Date published:09 Nov 2012
Word count:9359
Keywords:Booth-Maindonald-Smith method, functional data analysis, Hyndman-Ullah method, Lee-Carter method, Lee-Miller method, principal components analysis, random walk with drift
DOI:10.4054/DemRes.2012.27.21
 

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.

Author's Affiliation

Han Lin Shang - Australian National University, Australia [Email]

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» Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods
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