IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v20y2014i4p261-277n4.html
   My bibliography  Save this article

Uncertainty quantification of world population growth: A self-similar PDF model

Author

Listed:
  • Heinz Stefan

    (Department of Mathematics, University of Wyoming, 1000 East University Avenue, Laramie, WY 82071, USA)

Abstract

The uncertainty of world population growth represents a serious global problem. Existing methods for quantifying this uncertainty face a variety of questions. An essential problem of these methods is the lack of direct evidence for their validity, for example by means of comparisons with independent observations like measurements. A way to support the validity of such forecast methods is to validate these models with reference models, which play the role of independent observations. Desired properties of such a reference model are formulated here. A new reference world population model is formulated by a probabilistic extension of recent deterministic UN projections. This model is validated in terms of theory and observations: it is shown that the model has all desired properties of a reference model, and its predictions are very well supported by the known world population development from 1980 till 2010. Applications of this model as a reference model demonstrate the advantages of the stochastic world population model presented here.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:mcmeap:v:20:y:2014:i:4:p:261-277:n:4
    DOI: 10.1515/mcma-2014-0005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2014-0005
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2014-0005?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. F. C. Billari & R. Graziani & E. Melilli, 2012. "Stochastic population forecasts based on conditional expert opinions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 491-511, April.
    2. Graham M Turner, 2008. "A Comparison of the Limits to Growth with Thirty Years of Reality," Socio-Economics and the Environment in Discussion (SEED) Working Paper Series 2008-09, CSIRO Sustainable Ecosystems.
    3. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 2001. "The end of world population growth," Nature, Nature, vol. 412(6846), pages 543-545, August.
    4. Jakub Bijak & Arkadiusz Wiśniowski, 2010. "Bayesian forecasting of immigration to selected European countries by using expert knowledge," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 775-796, October.
    5. Maarten Alders & Nico Keilman & Harri Cruijsen, 2007. "Assumptions for long-term stochastic population forecasts in 18 European countries," European Journal of Population, Springer;European Association for Population Studies, vol. 23(1), pages 33-69, March.
    6. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
    7. 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.
    8. Sergei Scherbov & Wolfgang Lutz & Warren C. Sanderson, 2011. "The Uncertain Timing of Reaching 8 Billion, Peak World Population, and Other Demographic Milestones," Population and Development Review, The Population Council, Inc., vol. 37(3), pages 571-578, September.
    9. 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).
    10. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 2008. "The coming acceleration of global population ageing," Nature, Nature, vol. 451(7179), pages 716-719, February.
    11. 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.
    12. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 1997. "Doubling of world population unlikely," Nature, Nature, vol. 387(6635), pages 803-805, June.
    13. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454.
    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. 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.
    2. Bailey Fosdick & Adrian E. Raftery, 2014. "Regional probabilistic fertility forecasting by modeling between-country correlations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(35), pages 1011-1034.
    3. Meng Xu & Helge Brunborg & Joel E. Cohen, 2017. "Evaluating multi-regional population projections with Taylor’s law of mean–variance scaling and its generalisation," Journal of Population Research, Springer, vol. 34(1), pages 79-99, March.
    4. 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.
    5. 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.
    6. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    7. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
    8. 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.
    9. Tongzheng Pu & Chongxing Huang & Jingjing Yang & Ming Huang, 2023. "Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    10. Guy Abel & Jakub Bijak & Jonathan J. Forster & James Raymer & Peter W.F. Smith & Jackie S.T. Wong, 2013. "Integrating uncertainty in time series population forecasts: An illustration using a simple projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(43), pages 1187-1226.
    11. 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.
    12. Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.
    13. Warren C Sanderson & Sergei Scherbov & Patrick Gerland, 2017. "Probabilistic population aging," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-12, June.
    14. 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.
    15. 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.
    16. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.
    17. 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.
    18. 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.
    19. Nico Keilman, 2018. "Probabilistic demographic forecasts," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 16(1), pages 025-035.
    20. 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.

    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:bpj:mcmeap:v:20:y:2014:i:4:p:261-277:n:4. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.