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A Bayesian smoothing spline method for mortality modelling

Published online by Cambridge University Press:  15 May 2012

Arto Luoma ,
Anne Puustelli  and
Lasse Koskinen
Arto Luoma*
Affiliation:
University of Tampere, Finland
Anne Puustelli
Affiliation:
University of Tampere, Finland
Lasse Koskinen
Affiliation:
Financial Supervisory Authority of Finland and Helsinki School of Economics, Finland
*
*Correspondence to: Arto Luoma, School of Information Sciences, FIN-33014 University of Tampere, Finland. E-mail: arto.luoma@uta.fi
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Abstract

We propose a new method for two-dimensional mortality modelling. Our approach smoothes the data set in the dimensions of cohort and age using Bayesian smoothing splines. The method allows the data set to be imbalanced, since more recent cohorts have fewer observations. We suggest an initial model for observed death rates, and an improved model which deals with the numbers of deaths directly. Unobserved death rates are estimated by smoothing the data with a suitable prior distribution. To assess the fit and plausibility of our models we perform model checks by introducing appropriate test quantities. We show that our final model fulfils nearly all requirements set for a good mortality model.

Keywords

Cohort effectForecastingModel checkingParameter uncertaintyStochastic mortality model
Type
Papers
Information
Annals of Actuarial Science , Volume 6 , Issue 2 , 23 August 2012 , pp. 284 - 306
Copyright
Copyright © Institute and Faculty of Actuaries 2012

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