Modeling Mortality with a Bayesian Vector Autoregression
AbstractMortality 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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Bibliographic InfoPaper 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.
Length: 39 pages
Date of creation: Mar 2011
Date of revision:
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Mortality; parameter risk; vector auto-regression; Bayesian; Heligman-Pollard model;
Find related papers by 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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