New evaluations of simple models for small area population forecasts

Wilson, Tom (2014) New evaluations of simple models for small area population forecasts. Population, Space and Place, 21 4: 335-353. doi:10.1002/psp.1847

Author Wilson, Tom
Title New evaluations of simple models for small area population forecasts
Journal name Population, Space and Place   Check publisher's open access policy
ISSN 1544-8452
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1002/psp.1847
Open Access Status Not yet assessed
Volume 21
Issue 4
Start page 335
End page 353
Total pages 19
Place of publication Oxford, United Kingdom
Publisher John Wiley & Sons
Language eng
Abstract At the small area scale simple methods for forecasting total populations are often employed because of a lack of data for cohort-component models, concerns about the reliability of these models for forecasting small population totals, and resource constraints. To date, a select number of authors have assessed the forecast accuracy of several individual, averaged, and composite models. This paper extends this stream of work by evaluating a large number of models on new datasets. The aims of the paper are to examine the performance of (a) 10 individual forecasting models (some of which are well known; others less so); (b) averages of every combination of 2, 3, 4, and 5 of the individual models (627 in total); and (c) composite models based on population size and growth rates (200,000 in total). Do averaged and composite models outperform individual models? Using new small area population datasets, forecasts from 2001 to 2031 were produced for three case study countries, Australia, New Zealand, and England & Wales. Both forecast accuracy and credibility (avoidance of negatives; degree of constraining to state populations) were assessed in 2011; for 2031, just credibility was evaluated. Of the individual models, constant share of growth (positive shares only) and constant share of population performed the best. A small proportion of averaged and composite models outperformed the best individual models in forecast accuracy. Several recommendations for the practice of small area population forecasting are made.
Keyword Small area
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 24 March 2014.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 01 Apr 2014, 20:05:35 EST by Dr Tom Wilson on behalf of School of Geography, Planning & Env Management