IDEAS home Printed from https://ideas.repec.org/a/spr/joprea/v41y2024i1d10.1007_s12546-023-09326-7.html
   My bibliography  Save this article

Accuracy of small area mortality prediction methods: evidence from Poland

Author

Listed:
  • Agnieszka Orwat-Acedańska

    (University of Economics in Katowice)

Abstract

We investigate the forecasting accuracy of several simple methods for predicting mortality in small regional areas in Poland. We focus on methods that scale country-level forecasts appropriately and, therefore, can be used by official statistical agencies to improve population projections. We examine data from 379 sub-NUTS-3 districts in Poland for the period 2006–2019, divided into three subperiods. The first period is treated as the training sample and the latter two the testing subperiods. The mortality surface method delivers the most accurate forecasts of the mortality profiles whereas using the district-level standardized mortality rates (SMR) calculated for several broad age groups to scale the country-level mortality forecasts gives the best life expectancy at birth predictions. The latter approach is far better than using the NUTS-2-based standardized mortality rate (SMR), as practiced by the Polish statistical agency. For single age-groups predictions, the SMR-based methods deliver relatively accurate forecasts for young cohorts, but their forecasting accuracy deteriorates significantly with age.

Suggested Citation

  • Agnieszka Orwat-Acedańska, 2024. "Accuracy of small area mortality prediction methods: evidence from Poland," Journal of Population Research, Springer, vol. 41(1), pages 1-20, March.
    • Handle: RePEc:spr:joprea:v:41:y:2024:i:1:d:10.1007_s12546-023-09326-7
    DOI: 10.1007/s12546-023-09326-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12546-023-09326-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12546-023-09326-7?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. 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.
    2. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    3. Monica Alexander & Emilio Zagheni & Magali Barbieri, 2017. "A Flexible Bayesian Model for Estimating Subnational Mortality," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2025-2041, December.
    4. Joop de Beer, 2012. "Smoothing and projecting age-specific probabilities of death by TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(20), pages 543-592.
    5. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    6. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Khalaf-Allah, Marwa, 2011. "Bayesian Stochastic Mortality Modelling for Two Populations," ASTIN Bulletin, Cambridge University Press, vol. 41(1), pages 29-59, May.
    7. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    8. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    9. 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.
    10. Peter Congdon, 2009. "Life Expectancies for Small Areas: A Bayesian Random Effects Methodology," International Statistical Review, International Statistical Institute, vol. 77(2), pages 222-240, August.
    11. Sigurd Dyrting, 2020. "Smoothing migration intensities with P-TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(55), pages 1607-1650.
    12. Sergei Scherbov & Dalkhat Ediev, 2011. "Significance of life table estimates for small populations: Simulation-based study of estimation errors," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(22), pages 527-550.
    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. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    2. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    3. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2016. "Coherent modeling of male and female mortality using Lee–Carter in a complex number framework," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 130-137.
    4. Péter Vékás, 2020. "Rotation of the age pattern of mortality improvements in the European Union," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 1031-1048, September.
    5. Christina Bohk-Ewald & Marcus Ebeling & Roland Rau, 2017. "Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1559-1577, August.
    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," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    8. 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.
    9. 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.
    10. 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.
    11. repec:osf:socarx:8u34d_v1 is not listed on IDEAS
    12. Simon Schnürch & Torsten Kleinow & Ralf Korn, 2021. "Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model," Risks, MDPI, vol. 9(3), pages 1-32, March.
    13. Li, Hong & Lu, Yang, 2017. "Coherent Forecasting Of Mortality Rates: A Sparse Vector-Autoregression Approach," ASTIN Bulletin, Cambridge University Press, vol. 47(2), pages 563-600, May.
    14. Ameer Dharamshi & Magali Barbieri & Monica Alexander & Celeste Winant, 2025. "Jointly estimating subnational mortality for multiple populations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 52(3), pages 71-110.
    15. Kenneth Wong & Jackie Li & Sixian Tang, 2020. "A modified common factor model for modelling mortality jointly for both sexes," Journal of Population Research, Springer, vol. 37(2), pages 181-212, June.
    16. Jackie Li & Leonie Tickle & Nick Parr, 2016. "A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes," Journal of Population Research, Springer, vol. 33(4), pages 333-360, December.
    17. Snorre Jallbjørn & Søren Fiig Jarner, 2022. "Sex Differential Dynamics in Coherent Mortality Models," Forecasting, MDPI, vol. 4(4), pages 1-26, September.
    18. Ricarda Duerst & Jonas Schöley & Christina Bohk-Ewald, 2023. "A validation workflow for mortality forecasting," MPIDR Working Papers WP-2023-020, Max Planck Institute for Demographic Research, Rostock, Germany.
    19. 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.
    20. 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.
    21. 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.

    More about this item

    Keywords

    Mortality; Life expectancy; Forecasting; Small area; Mortality rate; Relational models;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:spr:joprea:v:41:y:2024:i:1:d:10.1007_s12546-023-09326-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.