Empirical Assessments and a Comparison with the Headship Rate Method

  • Yi Zeng
  • Kenneth C. Land
  • Danan Gu
  • Zhenglian Wang
Chapter
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 36)

Abstract

One useful way to validate a projection model and computer program is to project between two past dates for which the observations are known, and then compare the observed data with the projected data. We assessed the accuracy of the ProFamy method and program by projecting: (1) U.S. households by race from 1990 to 2000 (Zeng et al. 2006), (2) Chinese households by rural and urban areas from 1990 to 2000 (Zeng et al. 2008), and (3) Chinese households by rural and urban areas and Eastern, Middle, and Western regions from 2000 to 2010 (Zeng et al. 2013b).

Keywords

Forecast Error Housing Unit Mean Absolute Percent Error Household Type Family Household 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bell, M., & Cooper, J. (1990). Household forecasting: Replacing the headship rate model. Paper presented at the Fifth National Conference, Melbourne: Australian Population Association.Google Scholar
  2. Booth, H. (1984). Transforming Gompetrz’s function for fertility analysis: The development of a standard for the relational Gompertz function. Population Studies, 38, 495–506.Google Scholar
  3. Brass, W. (1974). Perspectives in population prediction: Illustrated by the statistics of England and Wales. Journal of the Royal Statistical Society, 137(A), 532–583.Google Scholar
  4. Brass, W. (1978). The relational Gompertz model of fertility by age of women. London: London School of Hygiene and Tropical Medicine.Google Scholar
  5. Campbell, P. R. (2002). Evaluating forecast error in state population projections using census 2000 counts (U. S. Census Bureau Population Division Working Paper Series No. 57). http://www.census.gov/population/www/documentation/twps0057/twps0057.html. Accessed 1 Oct 2011.
  6. Census Bureau, U. S. (1998). Statistical abstract of the United States: 1998. Washington, DC: GPO.Google Scholar
  7. Congressional Budget Office (CBO). (2004). Financing long-term care for the elderly. The Congress of the United States. http://www.cbo.gov/ftpdocs/54xx/doc5400/04-26-LongTermCare.pdf. Accessed 15 May 2006.
  8. Costa, D. L. (2000). Long-term declines in disability among older men: Medical care, public health, and occupational change (NBER Working Paper No. 7605). Cambridge, MA: National Bureau of Economic Research.Google Scholar
  9. Federal Interagency Forum Aging Related Statistics (FIFARS). (2004). Older Americans 2004: Key indicators of wellbeing. Federal Interagency Forum on Aging-Related Statistics. Washington, DC: U.S. Government Printing Office, November 2004.Google Scholar
  10. Federal Interagency Forum Aging Related Statistics (FIFARS). (2008). Older Americans 2008: Key indicators of wellbeing. Federal Interagency Forum on Aging-Related Statistics. Washington, DC: U.S. Government Printing Office, March 2008.Google Scholar
  11. Federal Interagency Forum Aging Related Statistics (FIFARS). (2010). Older Americans 2004: Key indicators of wellbeing. Federal Interagency Forum on Aging-Related Statistics. Washington, DC: U.S. Government Printing Office, July 2010.Google Scholar
  12. Guo, Z., Zhang, E., Gu, B., & Wang, F. (2003). Diversity of China’s fertility policy by policy fertility. Population Research, 27(5), 1–10 [in Chinese].Google Scholar
  13. Keyfitz, N. (1972). On future population. Journal of the American Statistical Association, 67, 347–363.CrossRefGoogle Scholar
  14. Lakdawalla, D., Goldman, D. P., Bhattacharya, J., Hurd, M. D., Joyce, G. F., & Panis, C. W. A. (2003). Forecasting the nursing home population. Medical Care, 41(1), 8–20.CrossRefGoogle Scholar
  15. Manton, K. G. (1982). Changing concepts of morbidity and mortality in the elderly population. Milbank Memorial Fund Quarterly, 60, 183–244.CrossRefGoogle Scholar
  16. Moffitt, R. (2000). Demographic change and public assistance expenditures. In A. J. Auerbach & R. D. Lee (Eds.), Demographic change and public policy (pp. 391–425). Cambridge: Cambridge University Press.Google Scholar
  17. Murphy, M. (1991). Modelling households: A synthesis. In M. J. Murphy & J. Hobcraft (Eds.), Population research in Britain (A supplement to population studies, Vol. 45, pp. 157–176). London: Population Investigation Committee, London School of Economics.Google Scholar
  18. National Center for Health Statistics (NCHS). (1998). Declines in teenage birth rates, 1991–97: National and State patterns. In S. J. Ventura, T. J. Mathews, & S. C. Curtin (Eds.), National Vital Statistics Report 47, No. 12. Hyattsville: National Center for Health Statistics.Google Scholar
  19. Natural Resources of Canada. (2004). Light-duty vehicle fuel efficiency scenario: model year 1990 to 2010. http://atlas.gc.ca/site/english/maps/climatechange/humanactivitiesemissions/methodologyprojection.html. Accessed 10 June 2010.
  20. Ng, S. T., Skitmore, M., & Wong, K. F. (2008). Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment, 43(6), 1171–1184.CrossRefGoogle Scholar
  21. Poston, D. L., & Duan, C. C. (2000). The current and projected distribution of the elderly and eldercare in the People’s Republic of China. Journal of Family Issues, 21, 714–732.CrossRefGoogle Scholar
  22. Robinson, K. (2007). Trends in health status and health care use among older women (Aging trends, Vol. 7). Hyattsville: National Center for Health Statistics.Google Scholar
  23. Schoen, R. (1981). The harmonic mean as the basis of a realistic two-sex marriage model. Demography, 18, 201–216.CrossRefGoogle Scholar
  24. Spain, D. (1997). Societal trends: The aging baby boom and women’s increased independence. Report prepared for the US Department of Transportation, DTFH 61-97-P-00314.Google Scholar
  25. Stupp, P. W. (1988). A general procedure for estimating intercensal age schedules. Population Index, 54, 209–234.CrossRefGoogle Scholar
  26. U.S. Census Bureau. (1996). Projections of the number of households and families in the United States: 1995 to 2010. U.S. Department of Commerce Economics and Statistics Administration, Current Population Reports (P25–1129). Washington, DC: U.S. Government Printing Office.Google Scholar
  27. Wang, Z. (2009a). Households and living arrangements forecasting and the associated database development for Southern California six counties and the whole Region, 2000–2040 (In response to RFP No. 09-043-IN of SCAG: Household Projection Model Development: Simulation Approach) (Technical report No. 09–02 of Households and Consumption Forecasting Inc.).Google Scholar
  28. Wang, Z. (2009b). Household projection and living arrangements projections for Minneapolis-St Paul region, 2000–2050 (Technical report No. 09–03 of Households and Consumption Forecasting Inc.).Google Scholar
  29. Wang, Z. (2011a). The 2011 updating of households and living arrangements forecasting for Southern California six counties and the whole region, 2000–2040. (Technical report No. 11–01 of Households and Consumption Forecasting Inc.).Google Scholar
  30. Wang, Z. (2011b). The 2011 updating of household projection and living arrangements projections for Minneapolis-St Paul region, 2000–2050. (Technical report No. 11–02 of Households and Consumption Forecasting Inc.).Google Scholar
  31. Zeng, Y., & George, L. K. (2010). Population ageing and old-age insurance in China. In D. Dannefer & C. Phillipson (Eds.), The SAGE handbook of social gerontology (pp. 420–430). London: Sage.Google Scholar
  32. Zeng, Y., Wang, Z., Ma, Z., & Chen, C. (2000). A simple method for estimating α and β: An extension of brass relational Gompertz fertility model. Population Research and Policy Review, 19(6), 525–549.CrossRefGoogle Scholar
  33. Zeng, Y., Land, K. C., Wang, Z., & Gu, D. (2006). U.S. family household momentum and dynamics–Extension of ProFamy method and application. Population Research and Policy Review, 25(1), 1–41.CrossRefGoogle Scholar
  34. Zeng, Y., Morgan, P., Wang, Z., Gu, D., & Yang, C. (2012b). A multistate life table analysis of union regimes in the United States– Trends and racial differentials, 1970–2002. Population Research and Policy Review, 31, 207–234.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Yi Zeng
    • 1
    • 2
  • Kenneth C. Land
    • 3
  • Danan Gu
    • 4
  • Zhenglian Wang
    • 5
    • 6
  1. 1.Center for Study of Aging and Human Development Medical SchoolDuke UniversityDurhamUSA
  2. 2.National School of Development Center for Healthy Aging and Development StudiesPeking UniversityBeijingChina
  3. 3.Department of Sociology and Center for Population Health and Aging Population Research InstituteDuke UniversityDurhamUSA
  4. 4.Population DivisionUnited NationsNew YorkUSA
  5. 5.Center for Population Health and Aging Population Research InstituteDuke UniversityDurhamUSA
  6. 6.Household and Consumption Forecasting, Inc.Chapel HillUSA

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