ARC Centre of Excellence in Population Ageing Research Working Paper 2011/5 Modeling Mortality with a Bayesian Vector Autoregression Carolyn Njenga and Michael Sherris* * Njenga is a doctoral student in the School of Risk and Actuarial at the University of New South Wales (UNSW) and a Research Assistant at the ARC Centre of Excellence in Population Ageing Research (CEPAR). Sherris is Professor of Actuarial Studies at UNSW and a CEPAR Chief Investigator. This paper can be downloaded without charge from the ARC Centre of Excellence in Population Ageing Research Working Paper Series available at www.cepar.edu.au Modeling Mortality with a Bayesian Vector Autoregression Carolyn Ndigwako Njenga Australian School of Business University of New South Wales, Sydney, NSW, 2052 Australia Email:
[email protected] Michael Sherris Australian School of Business University of New South Wales, Sydney, NSW, 2052 Email:
[email protected] 4th March 2011 Abstract Mortality risk models have been developed to capture trends and common fac- tors 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 im- provement from young ages to middle and then into older ages. The Heligman and Pollard (1980) model is a parametric model which captures the main features of period mortality tables and has parameters that are interpreted according to age range and effect on rates. Although time series techniques have been applied to model parameters in various parametric mortality models, there has been limited analysis of parameter risk using Bayesian techniques.