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Modeling Mortality with a Bayesian Vector Autoregression

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Author Info

  • 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)

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    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.

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    File URL: http://cepar.edu.au/media/48706/Modeling%20Mortality.pdf
    File Function: First version, 2011
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    Bibliographic Info

    Paper provided by ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales in its series Working Papers with number 201105.

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    Length: 39 pages
    Date of creation: Mar 2011
    Date of revision:
    Handle: RePEc:asb:wpaper:201105

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    Related research

    Keywords: Mortality; parameter risk; vector auto-regression; Bayesian; Heligman-Pollard model;

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    1. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper, Federal Reserve Bank of Atlanta 96-13, Federal Reserve Bank of Atlanta.
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