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

Han Lin Shang; Heather Booth; Rob Hyndman

doi:10.4054/DemRes.2011.25.5

Volume 25 - Article 5 | Pages 173–214

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

By Han Lin Shang, Heather Booth, Rob Hyndman

Date received:

9 Apr 2010

Date published:

15 Jul 2011

Word count:

8245

Keywords:

forecasting, forecasting time series, interval forecasts, Lee-Carter model, life expectancy, mortality forecasting, principal components analysis

DOI:

10.4054/DemRes.2011.25.5

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Abstract

Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.