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Modeling Mortality with a Bayesian Vector Autoregression

Author

Listed:
  • Carolyn Njenga

    (School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research, Australian School of Business, University of New South Wales)

  • Michael Sherris

    (School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research, Australian School of Business, University of New South Wales)

Abstract

Mortality risk models have been developed to capture trends and common factors driving mortality improvement. Multiple factor models take many forms and are often developed and fitted to older ages. In order to capture trends from young ages it is necessary to take into account the richer age structure of mortality improvement from young ages to middle and then into older ages.

Suggested Citation

  • Carolyn Njenga & Michael Sherris, 2011. "Modeling Mortality with a Bayesian Vector Autoregression," Working Papers 201105, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    • Handle: RePEc:asb:wpaper:201105
    as

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    File URL: http://cepar.edu.au/media/48706/Modeling%20Mortality.pdf
    File Function: First version, 2011
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    References listed on IDEAS

    as
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    Cited by:

    1. Wong, Chi Heem & Tsui, Albert K., 2015. "Forecasting life expectancy: Evidence from a new survival function," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 208-226.
    2. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 485564, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.

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    More about this item

    Keywords

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    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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