IDEAS home Printed from https://ideas.repec.org/p/han/dpaper/dp-579.html
   My bibliography  Save this paper

The Total Fertility Rate in Germany until 2040 - A Stochastic Principal Components Projection based on Age-specific Fertility Rates

Author

Listed:
  • Vanella, Patrizio

Abstract

Demographic change is one of the greatest challenges faced by Germany as well as a large part of Europe today. One of the main drivers of this change is the low fertility level, often referred to as the Total Fertility Rate (TFR), since the early 1970s. Therefore, on the one hand, while the total population is expected to decline, on the other hand, the relative share of the elderly in the total population is expected to increase. This poses a great challenge for the society in a wide range of aspects, most notably in the statutory pension fund. Therefore, it is important to gain an understanding of the future demographic development, in our case, the course of the TFR. Official forecasts often assume that the TFR will remain at a low level of 1.4 in the long run, which was already proven wrong in the publication of the 2014 data, which shows a TFR of 1.47. However, separate analysis of age-specific fertility lead to expected increases of the future TFR. This study presents a stochastic projection of the TFR based on econometric-statistical modeling of age-specific fertility rates over principal components. Simulation techniques not only generate the expected future TFR until the year 2040, but also provide point-wise prediction intervals which cover the future TFR with a probability of 95\% annually based on the current data set. The age-specific structure of the modeling procedure gives a detailed insight of the future development of the reproductive behavior for women in Germany, and therefore, is very informative with regard to possible political intervention with the scope of increasing the TFR. Moreover, the flexible structure of the model allows more sophisticated estimations of future outcome of certain political measures.

Suggested Citation

  • Vanella, Patrizio, 2016. "The Total Fertility Rate in Germany until 2040 - A Stochastic Principal Components Projection based on Age-specific Fertility Rates," Hannover Economic Papers (HEP) dp-579, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-579
    as

    Download full text from publisher

    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-579.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Ronald D., 1993. "Modeling and forecasting the time series of US fertility: Age distribution, range, and ultimate level," International Journal of Forecasting, Elsevier, vol. 9(2), pages 187-202, August.
    2. 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.
    3. Jürgen Dorbritz, 2008. "Germany: Family diversity with low actual and desired fertility," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(17), pages 557-598.
    4. Ševčíková, Hana & Alkema, Leontine & Raftery, Adrian, 2011. "bayesTFR: An R package for Probabilistic Projections of the Total Fertility Rate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i01).
    5. 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.
    6. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
    7. Carl Schmertmann & Emilio Zagheni & Joshua R. Goldstein & Mikko Myrskylä, 2014. "Bayesian Forecasting of Cohort Fertility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 500-513, June.
    8. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    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. Vanella, Patrizio, 2017. "Age- and Sex-Specific Fertility in Germany until the Year 2040 - The Impact of International Migration," Hannover Economic Papers (HEP) dp-606, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Fuchs, Johann & Söhnlein, Doris & Weber, Brigitte & Weber, Enzo, 2017. "Forecasting labour supply and population: an integrated stochastic model," IAB-Discussion Paper 201701, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Johann Fuchs & Doris Söhnlein & Brigitte Weber & Enzo Weber, 2018. "Stochastic Forecasting of Labor Supply and Population: An Integrated Model," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 33-58, February.
    4. Niall Newsham & Francisco Rowe, 2021. "Projecting the demographic impact of Syrian migration in a rapidly ageing society, Germany," Journal of Geographical Systems, Springer, vol. 23(2), pages 231-261, April.

    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. Francesco Billari & Rebecca Graziani & Eugenio Melilli, 2014. "Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1933-1954, October.
    3. Vanella, Patrizio, 2017. "Age- and Sex-Specific Fertility in Germany until the Year 2040 - The Impact of International Migration," Hannover Economic Papers (HEP) dp-606, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Asako Ohinata & Dimitrios Varvarigos, 2020. "Demographic Transition and Fertility Rebound in Economic Development," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(4), pages 1640-1670, October.
    5. 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.
    6. 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.
    7. 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.
    8. Heinz Stefan, 2014. "Uncertainty quantification of world population growth: A self-similar PDF model," Monte Carlo Methods and Applications, De Gruyter, vol. 20(4), pages 261-277, December.
    9. Carl Schmertmann & Emilio Zagheni & Joshua R. Goldstein & Mikko Myrskylä, 2014. "Bayesian Forecasting of Cohort Fertility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 500-513, June.
    10. 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.
    11. Ševčíková, Hana & Raftery, Adrian E., 2016. "bayesPop: Probabilistic Population Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i05).
    12. Jonathan Azose & Adrian Raftery, 2015. "Bayesian Probabilistic Projection of International Migration," Demography, Springer;Population Association of America (PAA), vol. 52(5), pages 1627-1650, October.
    13. Vanella, Patrizio & Deschermeier, Philipp, 2018. "A Probabilistic Cohort-Component Model for Population Forecasting - The Case of Germany," Hannover Economic Papers (HEP) dp-638, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    14. Patrizio Vanella & Philipp Deschermeier & Christina B. Wilke, 2020. "An Overview of Population Projections—Methodological Concepts, International Data Availability, and Use Cases," Forecasting, MDPI, vol. 2(3), pages 1-18, September.
    15. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    16. Heer, Burkhard & Polito, Vito & Wickens, Michael R., 2020. "Population aging, social security and fiscal limits," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    17. Mei Sang & Jing Jiang & Xin Huang & Feifei Zhu & Qian Wang, 2024. "Spatial and temporal changes in population distribution and population projection at county level in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    18. 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.
    19. 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.
    20. Dominik Paprotny, 2021. "Convergence Between Developed and Developing Countries: A Centennial Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(1), pages 193-225, January.

    More about this item

    Keywords

    Fertility Projection; Applied Principal Components Analysis; Applied Time Series Analysis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:han:dpaper:dp-579. 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: Heidrich, Christian (email available below). General contact details of provider: https://edirc.repec.org/data/fwhande.html .

    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.