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The development of uncertainty in national and subnational population projections: A New Zealand perspective
The development of uncertainty in national and subnational population projections: A New Zealand perspective
Abstract
Projections or forecasts of future population, and the age and sex structure of the population, are key inputs to decision-making. However, these projections have an associated uncertainty that is often underappreciated by decision-makers. Moreover, a decision-maker faced with multiple population projections has no clear basis on which to decide between alternative projections. In this chapter, we outline the sources of uncertainty in population projections. We then describe the development of population projection methods in New Zealand, where the representation of uncertainty has become central to official projections produced by Statistics New Zealand, as well as alternative projections produced by the National Institute of Demographic and Economic Analysis. Finally, we outline a model-averaging approach that can be used by decision-makers to combine the information from multiple independent population projections. This may provide a more accurate approach to the use of population projections in decision-making, without requiring substantial modelling capability. This approach is illustrated with the example of subnational areas for the Waikato Region of New Zealand.
Type
Chapter in Book
Type of thesis
Series
Citation
Cameron, M., Dunstan, K., & Cook, L. (2021). The development of uncertainty in national and subnational population projections: A New Zealand perspective. In Cochrane, W., Cameron, M. P., & Alimi, O. (Eds.), Labor markets, migration, and mobility: Essays in honor of Jacques Poot (pp. 197-217). Springer. https://doi.org/10.1007/978-981-15-9275-1
Date
2021
Publisher
Springer
Degree
Supervisors
Rights
This is an author’s accepted version of a chapter published in the book: The Development of Uncertainty in National and Subnational Population Projections: A New Zealand Perspective. © 2021 Springer.