Journal Article10.2139/SSRN.1396201
Backtesting Stochastic Mortality Models: An Ex-Post Evaluation of Multi-Period Ahead-Density Forecasts
TL;DR: In this paper, a back-testing framework was used to evaluate the forecasting performance of six different stochastic mortality models applied to English & Welsh male mortality data, and the results from applying this methodology suggest that the models perform adequately by most backtests, and that there is little difference between the performances of five of the models.
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Abstract: This study sets out a backtesting framework applicable to the multi-period-ahead forecasts from stochastic mortality models and uses it to evaluate the forecasting performance of six different stochastic mortality models applied to English & Welsh male mortality data. The models considered are: Lee-Carter’s 1992 one-factor model; a version of Renshaw-Haberman’s 2006 extension of the Lee-Carter model to allow for a cohort effect; the age-period-cohort model of Currie (2006), which is a simplified version of Renshaw-Haberman; Cairns, Blake and Dowd’s 2006 two-factor model; and two generalised versions of the latter with an added cohort effect. For the data set used herein the results from applying this methodology suggest that the models perform adequately by most backtests, and that there is little difference between the performances of five of the models. The remaining model, however, shows forecast instability. The study also finds that density forecasts that allow for uncertainty in the parameters of the mortality model are more plausible than forecasts that do not allow for such uncertainty.
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Citations
A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States
Andrew J. G. Cairns,Blake David,Kevin Dowd,Guy Coughlan,David Epstein,Alen Sen Kay Ong,Igor Balevich +6 more
TL;DR: In this paper, the authors compare eight stochastic models explaining improvements in mortality rates in England and Wales and in the United States and find that, for higher ages, an extension of the Cairns-Blake-Dowd (CBD) model that incorporates a cohort effect fits the English and Wales males data best, while for U.S. males data, the Renshaw and Haberman (RH) extension to the Lee and Carter model that also allows for a cohort effects provides the best fit.
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Mortality Density Forecasts: An Analysis of Six Stochastic Mortality Models
TL;DR: In this paper, the authors investigate the uncertainty of forecasts of future mortality generated by a number of previously proposed stochastic mortality models, with the conclusion that model risk can be significant.
Bayesian Stochastic Mortality Modelling for Two Populations
TL;DR: An Age-Period-Cohort model is proposed which incorporates a mean-reverting stochastic spread that allows for different trends in mortality improvement rates in the short-run, but parallel improvements in the long run.
Modelling and management of mortality risk: a review
TL;DR: In this article, a wide range of extrapolative stochastic mortality models have been proposed over the last 15 to 20 years and a number of models that are considered are framed in discrete time and place emphasis on the statistical aspects of modelling and forecasting.
Modelling and Management of Mortality Risk: A Review
TL;DR: In this article, a wide range of extrapolative stochastic mortality models have been proposed over the last 15-20 years and a number of models that are considered are framed in discrete time and place emphasis on the statistical aspects of modelling and forecasting.
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