IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v32y2016i3p726-735.html
   My bibliography  Save this article

Household forecasting: Preservation of age patterns

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207016000194
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2015.10.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. repec:hum:wpaper:sfb649dp2009-015 is not listed on IDEAS
    4. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    5. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    6. Sigurd Dyrting & Abraham Flaxman & Ethan Sharygin, 2022. "Reconstruction of age distributions from differentially private census data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2311-2329, December.
    7. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    8. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    9. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    10. Sigurd Dyrting, 2020. "Smoothing migration intensities with P-TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(55), pages 1607-1650.
    11. Søren Kjærgaard & Yunus Emre Ergemen & Marie-Pier Bergeron Boucher & Jim Oeppen & Malene Kallestrup-Lamb, 2019. "Longevity forecasting by socio-economic groups using compositional data analysis," CREATES Research Papers 2019-08, Department of Economics and Business Economics, Aarhus University.
    12. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    13. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.
    14. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.
    15. repec:hum:wpaper:sfb649dp2009-008 is not listed on IDEAS
    16. 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.
    17. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    18. Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214.
    19. Hanewald, Katja, 2009. "Lee-Carter and the macroeconomy," SFB 649 Discussion Papers 2009-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. repec:hum:wpaper:sfb649dp2015-007 is not listed on IDEAS
    21. Han Lin Shang & Rob J Hyndman & Heather Booth, 2010. "A comparison of ten principal component methods for forecasting mortality rates," Monash Econometrics and Business Statistics Working Papers 8/10, Monash University, Department of Econometrics and Business Statistics.
    22. Ugarte Montero, Andrey & Wagner, Joël, 2023. "On potential information asymmetries in long-term care insurance: A simulation study using data from Switzerland," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 230-241.
    23. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:726-735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.