IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/119600.html

Muti-Scenarios Population Projection for Algeria using R

Author

Listed:
  • Flici, Farid

Abstract

In this manual, we present a methodology of doing a multi-scenarios population projections for Algeria using R. The methodology used consists of the Cohort-Component Method. A detailled methodology of doing mono-scenario population projection in R was already presented in Flici (2020). Here, mortality Scenarios are defined using the predictive intervals of a stochastic forecast, while the Fertility scenarios are expert-based. Here, 5 scenarios are simulated, the Age-Specific fertility rates are then defined using the Lee-Carter Model for fertility (Lee,1993).Later on, we shows how to make dynamic visualizations of the multi-scenario projections.

Suggested Citation

  • Flici, Farid, 2020. "Muti-Scenarios Population Projection for Algeria using R," MPRA Paper 119600, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:119600
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/119600/1/MPRA_paper_119600.pdf
    File Function: original version
    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. 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.
    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. 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.
    2. Flici, Farid, 2020. "Population projections using R, including graphical dynamic presentations," MPRA Paper 119599, University Library of Munich, Germany.
    3. 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.
    4. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    5. Li, Li & Li, Han & Panagiotelis, Anastasios, 2025. "Boosting domain-specific models with shrinkage: An application in mortality forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 191-207.
    6. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    7. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 485564, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.
    8. Alan J. Auerbach & Ronald Lee, 2009. "Notional Defined Contribution Pension Systems in a Stochastic Context: Design and Stability," NBER Chapters, in: Social Security Policy in a Changing Environment, pages 43-68, National Bureau of Economic Research, Inc.
    9. 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.
    10. 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.
    11. Flici, Farrid, 2016. "Projection des taux de mortalité par âges pour la population algérienne [Forecasting The Age Specific Mortality Rates For The Algerian Population]," MPRA Paper 98784, University Library of Munich, Germany, revised Dec 2016.
    12. Prskawetz, A. & Kogel, T. & Sanderson, W.C. & Scherbov, S., 2007. "The effects of age structure on economic growth: An application of probabilistic forecasting to India," International Journal of Forecasting, Elsevier, vol. 23(4), pages 587-602.
    13. Chen, An & Li, Hong & Schultze, Mark, 2022. "Collective longevity swap: A novel longevity risk transfer solution and its economic pricing," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 227-249.
    14. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    15. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    16. Gómez-Ugarte, Ana C. & Chen, Irena & Acosta, Enrique & Basellini, Ugofilippo & Alburez-Gutierrez, Diego, 2025. "Accounting for uncertainty in conflict mortality estimation: An application to the Gaza War in 2023-2024," SocArXiv z4e7s_v1, Center for Open Science.
    17. 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.
    18. José A. Ortega & Hans-Peter Kohler, 2002. "Measuring low fertility: rethinking demographic methods," MPIDR Working Papers WP-2002-001, Max Planck Institute for Demographic Research, Rostock, Germany.
    19. Marie-Pier Bergeron-Boucher & James E. Oeppen & Vladimir Canudas-Romo & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    20. Shang, Han Lin & Haberman, Steven, 2025. "Forecasting age distribution of deaths: Cumulative distribution function transformation," Insurance: Mathematics and Economics, Elsevier, vol. 122(C), pages 249-261.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

    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:pra:mprapa:119600. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.