An Evaluation of Projection Methods for Detailed Small Area Projections: An Application and Validation to King County, Washington
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DOI: 10.1007/s11113-023-09848-1
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- Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
- David Swanson & Alan Schlottmann & Bob Schmidt, 2010. "Forecasting the Population of Census Tracts by Age and Sex: An Example of the Hamilton–Perry Method in Action," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 29(1), pages 47-63, February.
- Richelle Winkler & Rozalynn Klaas, 2012. "Residential segregation by age in the United States," Journal of Maps, Taylor & Francis Journals, vol. 8(4), pages 374-378, December.
- Hendrik P. van Dalen & Kène Henkens, 2011.
"Who fears and who welcomes population decline?,"
Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(13), pages 437-464.
- van Dalen, H.P. & Henkens, C.J.I.M., 2011. "Who fears and who welcomes population decline?," Other publications TiSEM 308b5629-3537-457c-8e86-5, Tilburg University, School of Economics and Management.
- Kenneth M. Johnson & Daniel T. Lichter, 2008. "Natural Increase: A New Source of Population Growth in Emerging Hispanic Destinations in the United States," Population and Development Review, The Population Council, Inc., vol. 34(2), pages 327-346, June.
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Keywords
Projections; Small areas; Race and ethnicity;All these keywords.
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