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Multi-country clustering-based forecasting of healthy life expectancy

Author

Listed:
  • Susanna Levantesi

    (Sapienza University of Rome)

  • Andrea Nigri

    (University of Foggia)

  • Gabriella Piscopo

    (University of Naples Federico II)

  • Alessandro Spelta

    (Pavia University)

Abstract

Healthy life expectancy (HLE) is an indicator that measures the number of years individuals at a given age are expected to live free of disease or disability. HLE forecasting is essential for planning the provision of health care to elderly populations and appropriately pricing Long Term Care insurance products. In this paper, we propose a methodology that simultaneously forecasts HLE for groups of countries and allows for investigating similarities in their HLE patterns. We firstly apply a functional data clustering to the multivariate time series of HLE at birth of different countries for the years 1990–2019 provided by the Global Burden of Disease Study. Three clusters are identified for both genders. Then, we carry out the HLE simultaneous forecasting of the populations within each cluster by a multivariate random walk with drift. Numerical results and the statistical significance of the parameters of the identified multivariate processes are shown. Demographic evidences on the different evolution of HLE between countries are commented.

Suggested Citation

  • Susanna Levantesi & Andrea Nigri & Gabriella Piscopo & Alessandro Spelta, 2023. "Multi-country clustering-based forecasting of healthy life expectancy," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 189-215, December.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01611-6
    DOI: 10.1007/s11135-022-01611-6
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    References listed on IDEAS

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    1. Jeng‐Min Chiou & Pai‐Ling Li, 2007. "Functional clustering and identifying substructures of longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 679-699, September.
    2. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    3. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    4. Mitsunori Kayano & Koji Dozono & Sadanori Konishi, 2010. "Functional Cluster Analysis via Orthonormalized Gaussian Basis Expansions and Its Application," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 211-230, September.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    7. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    8. C. Abraham & P. A. Cornillon & E. Matzner‐Løber & N. Molinari, 2003. "Unsupervised Curve Clustering using B‐Splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 581-595, September.
    9. 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.
    10. Charles Bouveyron & Julien Jacques, 2011. "Model-based clustering of time series in group-specific functional subspaces," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 281-300, December.
    11. 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.
    12. Imai, Kosuke & Soneji, Samir, 2007. "On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1199-1211, December.
    13. Andrea Nigri & Susanna Levantesi & Mario Marino, 2021. "Life expectancy and lifespan disparity forecasting: a long short-term memory approach," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(2), pages 110-133, February.
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