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Household forecasting: Preservation of age patterns

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  • Keilman, Nico

Abstract

We formulate a time series model of household dynamics for different age groups. We model the shares of the population who are in certain household positions (living alone, living with a partner, etc.). These household positions have very pronounced age patterns. The age profiles change slowly over time, due to changes in the home leaving behaviour of young adults, differences in survival rates of men and women, etc. When forecasting household positions to 2040, we want to preserve the characteristics of the age profiles. We test the Lee–Carter model and the Brass relational method using household data for the Netherlands for the period 1996–2010. Annual shares of the population by household position, age, and sex are modeled as random walks with adrift (RWD). While the Brass model has its limitations, it performs better than the Lee–Carter model in our application. The predicted age patterns based on the Brass model look more reasonable, because the Brass model is a two-parameter model, while the Lee–Carter model contains only one parameter. Also, the model parameters and standard errors of the Brass model are easier to estimate than those of the Lee–Carter model.

Suggested Citation

  • Keilman, Nico, 2016. "Household forecasting: Preservation of age patterns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 726-735.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:726-735
    DOI: 10.1016/j.ijforecast.2015.10.007
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    References listed on IDEAS

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    1. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    2. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    3. Solveig Christiansen & Nico Keilman, 2013. "Probabilistic household forecasts based on register data- the case of Denmark and Finland," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(43), pages 1263-1302.
    4. Juha Alho & Jukka Nyblom, 1997. "Mixed estimation of old-age mortality," Mathematical Population Studies, Taylor & Francis Journals, vol. 6(4), pages 319-330.
    5. Joop de Beer, 2011. "A new relational method for smoothing and projecting age-specific fertility rates: TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(18), pages 409-454.
    6. Joop de Beer, 2012. "Smoothing and projecting age-specific probabilities of death by TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(20), pages 543-592.
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    Cited by:

    1. Nico Keilman, 2017. "A combined Brass-random walk approach to probabilistic household forecasting: Denmark, Finland, and the Netherlands, 2011–2041," Journal of Population Research, Springer, vol. 34(1), pages 17-43, March.
    2. Ala-Karvia Urszula & Hozer-Koćmiel Marta & Misiak-Kwit Sandra & Staszko Barbara, 2018. "Is Poland Becoming Nordic? Changing Trends In Household Structures In Poland And Finland With The Emphasis On People Living Alone," Statistics in Transition New Series, Statistics Poland, vol. 19(4), pages 725-742, December.
    3. Urszula Ala-Karvia & Marta Hozer-Koćmiel & Sandra Misiak-Kwit & Barbara Staszko, 2018. "Is Poland Becoming Nordic? Changing Trends In Household Structures In Poland And Finland With The Emphasis On People Living Alone," Statistics in Transition New Series, Polish Statistical Association, vol. 19(4), pages 725-742, December.

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