Disaggregation in population forecasting: do we need it? And how to do it simply.

Area of Science:

• Demography
• Population Studies
• Statistical Modeling

Background:

• Forecasting vital rates (fertility and mortality) is complex, often involving either aggregate data or detailed age-specific rates.
• Existing methods may lack parsimony or flexibility in capturing the temporal evolution of demographic processes.
• A need exists for methods that balance simplicity with accuracy in demographic forecasting.

Purpose of the Study:

• To present a parsimonious method for reducing the dimensionality of vital rate forecasting.
• To model the temporal evolution of age schedules using a reduced set of parameters.
• To offer a flexible framework for demographic projections adaptable to various scenarios.

Main Methods:

• Developed a method to forecast vital rates by modeling age schedules with single parameters for fertility and mortality.
• Explored refinements including simpler model fitting and incorporation of lower bounds for forecasts.
• Investigated extensions such as disaggregation by sex/race and integrated regional forecasts.

Main Results:

• The core method effectively reduces forecasting complexity by focusing on key parameters.
• Refinements and extensions maintained the underlying simplicity and structure of the basic model.
• Various versions of the method produced remarkably similar and consistent forecasting results.

Conclusions:

• The proposed parsimonious modeling approach offers an effective strategy for vital rate forecasting.
• The method provides a flexible and robust framework adaptable to diverse demographic research and policy needs.
• Further exploration of alternate forecasting techniques, like state-space models, is supported within this framework.