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.

在对国家以下地区的死亡率进行建模时，重要的是要纳入亚人群之间的任何可能相关性，以提高预测的准确性。此外，地方以下一级的预测应在国家一级的整个预测中保持一致。在这项研究中，我们应用了一个分组的多元函数时间序列来预测澳大利亚区域和偏远地区的特定年龄死亡率，并使用各种方法来协调小组结构中的预测。通过将提前一步与提前五步的预测精度进行比较，我们提出的方法与分组单变量函数时间序列预测方法相比具有优势。因此，我们证明了具有相似死亡率模式的亚群的联合建模可以提高点预测的准确性。