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Dynamic modelling of life table data

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  • J. Janssen
  • C. H. Skiadas

Abstract

In this paper we formulate a dynamic model expressing the human life table data by using the first‐passage‐time theory for a stochastic process. The model is derived analytically and then is applied to the mortality data in Belgium and France. A stochastic simulation is also performed for the ‘health state function’ proposed and the related stochastic paths. Furthermore the implications of the proposed model and the results derived for pension funds and option theory are discussed.

Suggested Citation

  • J. Janssen & C. H. Skiadas, 1995. "Dynamic modelling of life table data," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 11(1), pages 35-49, March.
    • Handle: RePEc:wly:apsmda:v:11:y:1995:i:1:p:35-49
    DOI: 10.1002/asm.3150110106
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    Cited by:

    1. DIMOTIKALIS, YIANNIS & Skiadas, Christos H, 2021. "Direct Healthy Life Expectancy Estimates from Life Tables with a Sullivan Extension: The case of Brazil 2003," SocArXiv 4x5et, Center for Open Science.
    2. Shantanu Awasthi & Indranil SenGupta, 2020. "First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process," Papers 2006.07167, arXiv.org, revised Jan 2021.
    3. Christos H. Skiadas & Charilaos Skiadas, 2020. "The First Exit Time Stochastic Theory Applied to Estimate the Life-Time of a Complicated System," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1601-1611, December.
    4. Christos H. Skiadas & Charilaos Skiadas, 2015. "Exploring the State of a Stochastic System via Stochastic Simulations: An Interesting Inversion Problem and the Health State Function," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 973-982, December.
    5. Konstantinos N. Zafeiris, 2023. "Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis," Journal of Population Research, Springer, vol. 40(1), pages 1-31, March.

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