The choice of sample size for mortality forecasting: A bayesian learning approach

Forecasted mortality rates using mortality models proposed in the recent literature are sensitive to the sample size. In this paper we propose a method based on Bayesian learning to determine model-specific posterior distributions of the sample sizes. In particular, the sample size is included as an extra parameter in the parameter space of the mortality model, and its posterior distribution is obtained based on historical performance for different forecast horizons up to 20 years. Age- and gender-specific posterior distributions of sample sizes are computed. Our method is applicable to a large class of linear mortality models. As illustration, we focus on the first generation of the Lee–Carter model and the Cairns–Blake–Dowd model. Our method is applied to US and Dutch data. For both countries we find highly concentrated posterior distributions of the sample size that are gender- and age-specific. In the out-of-sample forecast analysis, the Bayesian model outperforms the original mortality models with fixed sample sizes in the majority of cases.

Netspar, Network for Studies on Pensions, Aging and Retirement, is a thinktank and knowledge network. Netspar is dedicated to promoting a wider understanding of the economic and social implications of pensions, aging and retirement in the Netherlands and Europe.

MORE ABOUT NETSPAR


Mission en strategy           •           Network           •           Organisation           •          Magazine
Board Brief            •            Actionplan 2019-2023           •           Researchagenda

ABOUT NETSPAR

Our partners

B20180327_logo_PGB-Pensioendiensten_grijswaarden
B20160708_aegon
B20160708_maastricht university
B20160708_B20160615_Stichting-van-de-Arbeid-logo
B20160708_radboud
View all partners