Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

详细信息    查看全文

• 作者：Han Lin Shang
• 关键词：Bottom ; up forecasts ; Hierarchical forecasting ; Optimal combination ; Reconciling forecasts ; Australian infant mortality rates
• 刊名：Population Research and Policy Review
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：36
• 期：1
• 页码：55-84
• 全文大小：
• 刊物类别：Humanities, Social Sciences and Law
• 刊物主题：Demography; Sociology, general; Population Economics;
• 出版者：Springer Netherlands
• ISSN：1573-7829
• 卷排序：36

文摘

Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion, or ethnicity. Forecasting mortality rates at the national and sub-national levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman et al. in, Comput Stat Data Anal 55(9):2579–2589, 2011). A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.