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A quantitative comparison of stochastic mortality models on Italian population data

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  • Carfora, M.F.
  • Cutillo, L.
  • Orlando, A.

Abstract

Mortality models play a basic role in the evaluation of longevity risk by demographers and actuaries. Their performance strongly depends on the different patterns shown by mortality data in different countries. A comprehensive quantitative comparison of the most used methods for forecasting mortality is presented, aimed at evaluating both the goodness of fit and the forecasting performance of these mortality models on Italian demographic data. First, the classical Lee–Carter model is compared to some generalizations that change the order of Singular Value Decomposition approximation and include cohort effects. Then one-way and two-way functional data approaches are considered. Such an analysis extends the current literature on Italian mortality data, on both the number of considered models and their rigorous assessment. Results indicate that generally functional models outperform the classical ones; unfortunately, even if the cohort effect is quite substantial, a suitable procedure for its robust and efficient evaluation is yet to be proposed. To this end, a viable correction for cohort effects is suggested and its performance tested on some of the presented models.

Suggested Citation

  • Carfora, M.F. & Cutillo, L. & Orlando, A., 2017. "A quantitative comparison of stochastic mortality models on Italian population data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 198-214.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:198-214
    DOI: 10.1016/j.csda.2017.03.012
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    Cited by:

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    2. Sizhe Chen & Han Lin Shang & Yang Yang, 2025. "Is the age pension in Australia sustainable and fair? Evidence from forecasting the old-age dependency ratio using the Hamilton-Perry model," Journal of Population Research, Springer, vol. 42(1), pages 1-27, March.
    3. Maria Francesca Carfora & Albina Orlando, 2023. "A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19," Data, MDPI, vol. 8(6), pages 1-12, June.
    4. Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024. "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 114-133.
    5. Min Le & Xinrong Xiao & Dragan Pamučar & Qianling Liang, 2021. "A Study on Fiscal Risk of China’s Employees Basic Pension System under Longevity Risk," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    6. Gisou Díaz-Rojo & Ana Debón & Jaime Mosquera, 2020. "Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    7. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.

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