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Forecasting Australian subnational age-specific mortality rates

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  • Han Lin Shang

    (Macquarie University)

  • Yang Yang

    (Australian National University)

Abstract

When modeling sub-national mortality rates, it is important to incorporate any possible correlation among sub-populations to improve forecast accuracy. Moreover, forecasts at the sub-national level should aggregate consistently across the forecasts at the national level. In this study, we apply a grouped multivariate functional time series to forecast Australian regional and remote age-specific mortality rates and reconcile forecasts in a group structure using various methods. Our proposed method compares favorably to a grouped univariate functional time series forecasting method by comparing one-step-ahead to five-step-ahead point forecast accuracy. Thus, we demonstrate that joint modeling of sub-populations with similar mortality patterns can improve point forecast accuracy.

Suggested Citation

  • Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    • Handle: RePEc:spr:joprea:v:38:y:2021:i:1:d:10.1007_s12546-020-09250-0
    DOI: 10.1007/s12546-020-09250-0
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    Cited by:

    1. Ufuk Beyaztas & Hanlin Shang, 2022. "Machine-Learning-Based Functional Time Series Forecasting: Application to Age-Specific Mortality Rates," Forecasting, MDPI, vol. 4(1), pages 1-15, March.

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