IDEAS home Printed from https://ideas.repec.org/a/eto/stanov/v49y2011i2id127.html

Functional Data Analysis in Forecasting Serbian Fertility

Author

Listed:
  • Vladimir Nikitović

    (Demographic Research Centre)

Abstract

A new approach, combining functional data analysis and principal components decomposition in order to forecasting demographic rates, introduced recently by Hyndman and his associates, is tested on official data series of Serbian age-specific fertility rates available for period 1950-2009. The original concept of the method with its extensions and improvements is applied to region-specific data for the country (Central Serbia and Vojvodina). One of the most important benefits of the method reflected in confirmation that is essentially to model and forecast more than one principal component in order to adequately address sources of variation in fertility. Similarly, modelling and forecasting fertility rates with regards to age and not total fertility rates shows how important it is for the recognized tendency of postponing childbearing in Serbia to be included in coefficients of functional time series. Besides, the method is based completely on evaluation of historical data, without subjective views of forecasters having to be taken into account. Coherent functional product-ratio forecasts of two regions proved to be highly convergent on the long-term not allowing for outliers to contaminate the forecast.

Suggested Citation

  • Vladimir Nikitović, 2011. "Functional Data Analysis in Forecasting Serbian Fertility," Stanovnistvo, Institute of Social Sciences, Belgrade, Serbia, vol. 49(2), pages 73-89, December.
    • Handle: RePEc:eto:stanov:v:49:y:2011:i:2:id:127
    DOI: 10.2298/STNV1102073N
    as

    Download full text from publisher

    File URL: https://stnv.idn.org.rs/STNV/article/view/127
    File Function: Abstract page
    Download Restriction: no
    File URL: https://stnv.idn.org.rs/STNV/article/download/127/121
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.2298/STNV1102073N?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. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    2. Tom Wilson & Martin Bell, 2004. "Australia's uncertain demographic future," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 11(8), pages 195-234.
    3. 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.
    4. 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.
    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. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. 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.
    3. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    4. 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.
    5. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    6. José Rafael Caro-Barrera & María de los Baños García-Moreno García & Manuel Pérez-Priego, 2022. "Projecting Spanish fertility at regional level: A hierarchical Bayesian approach," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-27, October.
    7. Hong Li & Johnny Siu-Hang Li, 2017. "Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1073-1095, June.
    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. Feng, Lingbing & Shi, Yanlin & Chang, Le, 2021. "Forecasting mortality with a hyperbolic spatial temporal VAR model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 255-273.
    10. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    11. Ana Debón & Steven Haberman & Francisco Montes & Edoardo Otranto, 2021. "Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    12. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    13. Shaokang Wang & Han Lin Shang & Leonie Tickle & Han Li, 2024. "Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing," Risks, MDPI, vol. 12(7), pages 1-15, July.
    14. Yang, Yang & Shang, Han Lin & Raymer, James, 2024. "Forecasting Australian fertility by age, region, and birthplace," International Journal of Forecasting, Elsevier, vol. 40(2), pages 532-548.
    15. Deschermeier Philipp, 2014. "Prognose der Anzahl der Erwerbspersonen: Eine Vorausberechnung auf Basis der Funktionalen Datenanalyse am Beispiel der Metropolregion Rhein-Neckar," ZFW – Advances in Economic Geography, De Gruyter, vol. 58(1), pages 50-65, October.
    16. Rob J Hyndman & Yijun Zeng & Han Lin Shang, 2020. "Forecasting the Old-Age Dependency Ratio to Determine a Sustainable Pension Age," Monash Econometrics and Business Statistics Working Papers 31/20, Monash University, Department of Econometrics and Business Statistics.
    17. Antonio Elías & Raúl Jiménez & J. E. Yukich, 2023. "Localization processes for functional data analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 485-517, June.
    18. repec:osf:socarx:8u34d_v1 is not listed on IDEAS
    19. Yuan Gao & Han Lin Shang, 2017. "Multivariate Functional Time Series Forecasting: Application to Age-Specific Mortality Rates," Risks, MDPI, vol. 5(2), pages 1-18, March.
    20. Shang, Han Lin & Smith, Peter W.F. & Bijak, Jakub & Wiśniowski, Arkadiusz, 2016. "A multilevel functional data method for forecasting population, with an application to the United Kingdom," International Journal of Forecasting, Elsevier, vol. 32(3), pages 629-649.
    21. Shang, Han Lin & Hyndman, Rob.J., 2011. "Nonparametric time series forecasting with dynamic updating," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1310-1324.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eto:stanov:v:49:y:2011:i:2:id:127. 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: Marko Galjak (email available below). General contact details of provider: https://stnv.idn.org.rs/STNV .

    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.