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.
Original language | English |
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Pages (from-to) | 1-24 |
Number of pages | 24 |
Journal | Journal of Population Research |
Volume | 38 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Keywords
- Australian regional mortality rates
- Forecast reconciliation
- Hierarchical/grouped time series
- Multivariate functional principal component analysis