Multivariate time series modeling, estimation and prediction of mortalities
Number of Authors: 2
2015 (English)In: Insurance, Mathematics & Economics, ISSN 0167-6687, E-ISSN 1873-5959, Vol. 65, 156-171 p.Article in journal (Refereed) Published
We introduce a mixed regression model for mortality data which can be decomposed into a deterministic trend component explained by the covariates age and calendar year, a multivariate Gaussian time series part not explained by the covariates, and binomial risk. Data can be analyzed by means of a simple logistic regression model when the multivariate Gaussian time series component is absent and there is no overdispersion. In this paper we rather allow for overdispersion and the mixed regression model is fitted to mortality data from the United States and Sweden, with the aim to provide prediction and intervals for future mortality and annuity premium, as well as smoothing historical data, using the best linear unbiased predictor. We find that the form of the Gaussian time series has a large impact on the width of the prediction intervals, and it poses some new questions on proper model selection.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Place, publisher, year, edition, pages
2015. Vol. 65, 156-171 p.
Best linear unbiased predictor, Generalized least squares, Longevity, Mortality prediction, Multivariate time series, Overdispersion
Economics and Business Mathematics Sociology
IdentifiersURN: urn:nbn:se:su:diva-125810DOI: 10.1016/j.insmatheco.2015.09.013ISI: 000367109800017OAI: oai:DiVA.org:su-125810DiVA: diva2:896381