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Incorporating big microdata in life table construction: A hypothesis-free estimator

Author

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  • Lledó, Josep
  • Pavía, Jose M.
  • Morillas-Jurado, Francisco G.

Abstract

The IT revolution, now more than ever, offers a cheaper and faster way to collect, store, transmit and process data. Detailed microdata of dates of death, migration and birth are already becoming available for general populations. In this paper, we develop within the family of period-based estimators a new, assumption-free estimator for constructing life tables. The estimator proposed exploits all the detailed data available and is free of the theoretical inconsistencies that the estimators currently used by most official statistical agencies have. We compute the proposed estimator for a real database and test the suitability of the hypotheses on which the estimators used so far rely. The hypothesis of uniform distribution of birthdays is proven to be inadequate and the one having the largest impact on the estimated probabilities. Given its influence on public pension systems and life insurances, we advocate for adopting the more efficient approaches proposed in this paper.

Suggested Citation

  • Lledó, Josep & Pavía, Jose M. & Morillas-Jurado, Francisco G., 2019. "Incorporating big microdata in life table construction: A hypothesis-free estimator," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 138-150.
    • Handle: RePEc:eee:insuma:v:88:y:2019:i:c:p:138-150
    DOI: 10.1016/j.insmatheco.2019.06.005
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    References listed on IDEAS

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    1. Andrew J. G. Cairns & David Blake & Kevin Dowd & Amy R. Kessler, 2016. "Phantoms never die: living with unreliable population data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 975-1005, October.
    2. Stéphane Loisel, 2010. "Understanding, Modeling and Managing Longevity Risk: Key Issues and Main Challenges," Post-Print hal-00517902, HAL.
    3. Olivier Deschênes & Enrico Moretti, 2009. "Extreme Weather Events, Mortality, and Migration," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 659-681, November.
    4. Blake, D. & Cairns, A. J. G. & Dowd, K., 2006. "Living with Mortality: Longevity Bonds and Other Mortality-Linked Securities," British Actuarial Journal, Cambridge University Press, vol. 12(1), pages 153-197, March.
    5. 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.
    6. Johnny Li & Mary Hardy, 2011. "Measuring Basis Risk in Longevity Hedges," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 177-200.
    7. Pitacco, Ermanno & Denuit, Michel & Haberman, Steven & Olivieri, Annamaria, 2009. "Modelling Longevity Dynamics for Pensions and Annuity Business," OUP Catalogue, Oxford University Press, number 9780199547272.
    8. Steven Ruggles, 2014. "Big Microdata for Population Research," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 287-297, February.
    9. Börger, Matthias & Fleischer, Daniel & Kuksin, Nikita, 2014. "Modeling The Mortality Trend Under Modern Solvency Regimes," ASTIN Bulletin, Cambridge University Press, vol. 44(1), pages 1-38, January.
    10. Arkadiusz Wiśniowski & Jonathan J. Forster & Peter W. F. Smith & Jakub Bijak & James Raymer, 2016. "Integrated modelling of age and sex patterns of European migration," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1007-1024, October.
    11. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    12. Leonid Gavrilov & Natalia Gavrilova, 2011. "Mortality Measurement at Advanced Ages," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(3), pages 432-447.
    13. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Rates of mortality; Date of births; Period-based estimators; Big data; Spain;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

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