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A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes

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  • Jackie Li

    (Macquarie University)

  • Leonie Tickle

    (Macquarie University)

  • Nick Parr

    (Macquarie University)

Abstract

Mortality forecasts are critically important inputs to the consideration of a range of demographically-related policy challenges facing governments in more developed countries. While methods for jointly forecasting mortality for sub-populations offer the advantage of avoiding undesirable divergence in the forecasts of related populations, little is known about whether they improve forecast accuracy. Using mortality data from ten populations, we evaluate the data fitting and forecast performance of the Poisson common factor model (PCFM) for projecting both sexes’ mortality jointly against the Poisson Lee–Carter model applied separately to each sex. We find that overall the PCFM generates the more desirable results. Firstly, the PCFM ensures that the projected male-to-female ratio of death rates at each age converges to a constant in the long run. Secondly, using out-of-sample analysis, we find that the PCFM provides more accurate projection of the sex ratios of death rates, with the advantage being greater for longer-term forecasts. Thus the PCFM offers a viable and sensible means for coherently forecasting the mortality of both sexes. There are also significant financial implications in allowing for the co-movement of mortality of females and males properly.

Suggested Citation

  • Jackie Li & Leonie Tickle & Nick Parr, 2016. "A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes," Journal of Population Research, Springer, vol. 33(4), pages 333-360, December.
  • Handle: RePEc:spr:joprea:v:33:y:2016:i:4:d:10.1007_s12546-016-9173-0
    DOI: 10.1007/s12546-016-9173-0
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    2. Kenneth Wong & Jackie Li & Sixian Tang, 2020. "A modified common factor model for modelling mortality jointly for both sexes," Journal of Population Research, Springer, vol. 37(2), pages 181-212, June.

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