IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v9y2021i3p51-d514193.html
   My bibliography  Save this article

Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models

Author

Listed:
  • Anthony Medford

    (Interdiscliplinary Centre on Population Dynamics, University of Southern Denmark, 5000 Odense C, Denmark)

Abstract

Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best practice life expectancy. This class of model represents a neat and coherent framework for modeling time series extremes. The Gumbel distribution accounts for the extreme nature of best practice life expectancy, while the AR structure accounts for the temporal dependence in the time series. Model diagnostics and simulation results indicate that these models present a viable alternative to Gaussian AR(1) models when dealing with time series of extremes and merit further exploration.

Suggested Citation

  • Anthony Medford, 2021. "Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models," Risks, MDPI, vol. 9(3), pages 1-10, March.
  • Handle: RePEc:gam:jrisks:v:9:y:2021:i:3:p:51-:d:514193
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/9/3/51/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/9/3/51/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jackie Li & Jia Liu, 2020. "A modified extreme value perspective on best-performance life expectancy," Journal of Population Research, Springer, vol. 37(4), pages 345-375, December.
    2. Jacques Vallin & France Meslé, 2009. "The Segmented Trend Line of Highest Life Expectancies," Population and Development Review, The Population Council, Inc., vol. 35(1), pages 159-187, March.
    3. Anthony Medford & James W. Vaupel, 2020. "Extremes are not normal: a reminder to demographers," Journal of Population Research, Springer, vol. 37(1), pages 91-106, March.
    4. Anne‐Laure Fougères & John P. Nolan & Holger Rootzén, 2009. "Models for Dependent Extremes Using Stable Mixtures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 42-59, March.
    5. Martin Crowder, 1998. "A Multivariate Model for Repeated Failure Time Measurements," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 53-67, March.
    6. Anthony Medford, 2017. "Best-practice life expectancy: An extreme value approach," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(34), pages 989-1014.
    7. Jia Liu & Jackie Li, 2019. "Beyond the highest life expectancy: construction of proxy upper and lower life expectancy bounds," Journal of Population Research, Springer, vol. 36(2), pages 159-181, June.
    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. Jackie Li & Jia Liu, 2020. "A modified extreme value perspective on best-performance life expectancy," Journal of Population Research, Springer, vol. 37(4), pages 345-375, December.
    2. Andrea Nigri & Elisabetta Barbi & Susanna Levantesi, 2022. "The relay for human longevity: country-specific contributions to the increase of the best-practice life expectancy," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4061-4073, December.
    3. Anthony Medford & James W. Vaupel, 2020. "Extremes are not normal: a reminder to demographers," Journal of Population Research, Springer, vol. 37(1), pages 91-106, March.
    4. Jia Liu & Jackie Li, 2019. "Beyond the highest life expectancy: construction of proxy upper and lower life expectancy bounds," Journal of Population Research, Springer, vol. 36(2), pages 159-181, June.
    5. Anne‐Laure Fougères & John P. Nolan & Holger Rootzén, 2009. "Models for Dependent Extremes Using Stable Mixtures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 42-59, March.
    6. Casper Worm Hansen & Holger Strulik, 2017. "Life expectancy and education: evidence from the cardiovascular revolution," Journal of Economic Growth, Springer, vol. 22(4), pages 421-450, December.
    7. Robert E. Melchers & Mukshed Ahammed, 2021. "Estimating the Long-Term Reliability of Steel and Cast Iron Pipelines Subject to Pitting Corrosion," Sustainability, MDPI, vol. 13(23), pages 1-10, November.
    8. Sugeng Setyadi & Saharuddin Didu & Lili Indriyani & Ananda Kurnia Fitri & Anita Wiidiastuti, 2023. "Modeling Life Expectancy in Indonesia Using System GMM Model," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 25(1), pages 83-98, June.
    9. Enkelejd Hashorva & Simone A. Padoan & Stefano Rizzelli, 2021. "Multivariate extremes over a random number of observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 845-880, September.
    10. Nadarajah Saralees & Kotz Samuel, 2006. "Determination of Software Reliability based on Multivariate Exponential, Lomax and Weibull Models," Monte Carlo Methods and Applications, De Gruyter, vol. 12(5), pages 447-459, November.
    11. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    12. Kieron J. Barclay & Mikko Myrskylä, 2016. "Parental age and offspring mortality: negative effects of reproductive aging are outweighed by secular increases in longevity," MPIDR Working Papers WP-2016-011, Max Planck Institute for Demographic Research, Rostock, Germany.
    13. Jacie Jia Liu, 2021. "A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand," Risks, MDPI, vol. 9(1), pages 1-18, January.
    14. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    15. Anthony Medford & Kaare Christensen & Axel Skytthe & James W. Vaupel, 2019. "A Cohort Comparison of Lifespan After Age 100 in Denmark and Sweden: Are Only the Oldest Getting Older?," Demography, Springer;Population Association of America (PAA), vol. 56(2), pages 665-677, April.
    16. Jackie Li & Jia Liu & Adam Butt, 2024. "A systematic vector autoregressive framework for modeling and forecasting mortality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2279-2297, September.
    17. F. Peters & J. P. Mackenbach & W. J. Nusselder, 2016. "Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 687-702, December.
    18. Michal Engelman & Vladimir Canudas‐Romo & Emily M. Agree, 2010. "The Implications of Increased Survivorship for Mortality Variation in Aging Populations," Population and Development Review, The Population Council, Inc., vol. 36(3), pages 511-539, September.
    19. Søren Kjærgaard & Vladimir Canudas-Romo, 2017. "Potential support ratios: Cohort versus period perspectives," Population Studies, Taylor & Francis Journals, vol. 71(2), pages 171-186, May.
    20. Segers, Johan, 2012. "Max-Stable Models For Multivariate Extremes," LIDAM Discussion Papers ISBA 2012011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    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:gam:jrisks:v:9:y:2021:i:3:p:51-:d:514193. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.