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PARSIMONIOUS PARAMETERIZATION OF AGE-PERIOD-COHORT MODELS BY BAYESIAN SHRINKAGE

Published online by Cambridge University Press:  20 September 2017

Gary Venter  and
Şule Şahın
Gary Venter
Affiliation:
School of Risk and Actuarial Studies, Business School, University of New South Wales, Sydney, NSW 2052, Australia Actuarial Sciences Program, School of Professional Studies, Columbia University, New York, NY 10027, USA, E-Mail: gary.venter@gmail.com
Şule Şahın*
Affiliation:
Department of Actuarial Sciences, Hacettepe University, Ankara 06800, Turkey Institute for Financial and Actuarial Mathematics, University of Liverpool, Liverpool, L69 3BX, UK
*
E-Mail: sule@hacettepe.edu.tr
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Abstract

Age-period-cohort models used in life and general insurance can be over-parameterized, and actuaries have used several methods to avoid this, such as cubic splines. Regularization is a statistical approach for avoiding over-parameterization, and it can reduce estimation and predictive variances compared to MLE. In Markov Chain Monte Carlo (MCMC) estimation, regularization is accomplished by the use of mean-zero priors, and the degree of parsimony can be optimized by numerically efficient out-of-sample cross-validation. This provides a consistent framework for comparing a variety of regularized MCMC models, such as those built with cubic splines, linear splines (as ours is), and the limiting case of non-regularized estimation. We apply this to the multiple-trend model of Hunt and Blake (2014).

Keywords

Age-period-cohort modelsmortality trendsMCMCregularizationshrinkage priors
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 48 , Issue 1 , January 2018 , pp. 89 - 110
Copyright
Copyright © Astin Bulletin 2017 

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Footnotes

*

Şule Şahın's name has been corrected. An erratum notice detailing this change was also published (DOI: 10.1017/asb.2017.39).

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