IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v20y2009i25.html
   My bibliography  Save this article

Graduating the age-specific fertility pattern using Support Vector Machines

Author

Listed:
  • Anastasia Kostaki

    (Athens University of Economics and Business)

  • Javier Moguerza

    (Universidad Rey Juan Carlos)

  • Alberto Olivares

    (Universidad Rey Juan Carlos)

  • Stelios Psarakis

    (Athens University of Economics and Business)

Abstract

A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM) is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

Suggested Citation

  • Anastasia Kostaki & Javier Moguerza & Alberto Olivares & Stelios Psarakis, 2009. "Graduating the age-specific fertility pattern using Support Vector Machines," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(25), pages 599-622.
  • Handle: RePEc:dem:demres:v:20:y:2009:i:25
    DOI: 10.4054/DemRes.2009.20.25
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol20/25/20-25.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.4054/DemRes.2009.20.25?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
    ---><---

    References listed on IDEAS

    as
    1. Jan Hoem & Dan Madien & Jørgen Nielsen & Else-Marie Ohlsen & Hans Hansen & Bo Rennermalm, 1981. "Experiments in modelling recent Danish fertility curves," Demography, Springer;Population Association of America (PAA), vol. 18(2), pages 231-244, May.
    2. Pearce, N.D. & Wand, M.P., 2006. "Penalized Splines and Reproducing Kernel Methods," The American Statistician, American Statistical Association, vol. 60, pages 233-240, August.
    3. Carl Schmertmann, 2003. "A system of model fertility schedules with graphically intuitive parameters," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 9(5), pages 81-110.
    Full references (including those not matched with items on IDEAS)

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Carl Schmertmann, 2021. "D-splines: Estimating rate schedules using high-dimensional splines with empirical demographic penalties," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(45), pages 1085-1114.
    3. Gianni Corsetti & Marco Marsili, 2013. "Previsioni stocastiche della popolazione nell’ottica di un Istituto Nazionale di Statistica," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(2-3), pages 5-29.
    4. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    5. Mikko Myrskylä & Joshua R. Goldstein, 2010. "Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility," MPIDR Working Papers WP-2010-013, Max Planck Institute for Demographic Research, Rostock, Germany.
    6. Paraskevi Peristera & Anastasia Kostaki, 2007. "Modeling fertility in modern populations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 16(6), pages 141-194.
    7. Mikko Myrskylä & Joshua R. Goldstein & Yen-hsin Alice Cheng, 2012. "New cohort fertility forecasts for the developed world," MPIDR Working Papers WP-2012-014, Max Planck Institute for Demographic Research, Rostock, Germany.
    8. 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.
    9. Cristina Rueda-Sabater & Pedro Alvarez-Esteban, 2008. "The analysis of age-specific fertility patterns via logistic models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1053-1070.
    10. Yuan Cheng & Xuehui Han, 2013. "Does large volatility help?—stochastic population forecasting technology in explaining real estate price process," Journal of Population Economics, Springer;European Society for Population Economics, vol. 26(1), pages 323-356, January.
    11. Nico Keilman & Dinh Quang Pham, 2000. "Predictive Intervals for Age-Specific Fertility," European Journal of Population, Springer;European Association for Population Studies, vol. 16(1), pages 41-65, March.
    12. Ezra Gayawan & Samson B. Adebayo & Reuben A. Ipinyomi & Benjamin Oyejola, 2010. "Modeling fertility curves in Africa," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(10), pages 211-236.
    13. Wong, Raymond K.W. & Zhang, Xiaoke, 2019. "Nonparametric operator-regularized covariance function estimation for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 131-144.
    14. Alan Marshall & Paul Norman & Ian Plewis, 2013. "Applying Relational Models to the Graduation of Disability Schedules [Application de modèles relationnels pour le lissage de schémas d’incapacités]," European Journal of Population, Springer;European Association for Population Studies, vol. 29(4), pages 467-491, November.
    15. Joanne Ellison & Erengul Dodd & Jonathan J. Forster, 2020. "Forecasting of cohort fertility under a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 829-856, June.
    16. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 537-558, August.
    17. Rebecca Folkman Gleditsch & Astri Syse, 2020. "Ways to project fertility in Europe. Perceptions of current practices and outcomes," Discussion Papers 929, Statistics Norway, Research Department.
    18. Carl Schmertmann, 2003. "A system of model fertility schedules with graphically intuitive parameters," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 9(5), pages 81-110.
    19. Mikko Myrskylä & Joshua Goldstein, 2013. "Probabilistic Forecasting Using Stochastic Diffusion Models, With Applications to Cohort Processes of Marriage and Fertility," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 237-260, February.
    20. Rueda, Cristina & Rodríguez, Pilar, 2010. "State space models for estimating and forecasting fertility," International Journal of Forecasting, Elsevier, vol. 26(4), pages 712-724, October.

    More about this item

    Keywords

    age patterns of fertility; graduation techniques; support vector machines; parametric models of fertility;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:dem:demres:v:20:y:2009:i:25. 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: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    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.