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Risks 2016, 4(4), 45;

Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models

Dipartimento di Statistica e Matematica Applicata, corso Unione Sovietica 218 bis, Torino 10134, Italy
Academic Editor: Qihe Tang
Received: 29 August 2016 / Revised: 13 November 2016 / Accepted: 25 November 2016 / Published: 2 December 2016
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In this paper, we quantitatively compare the forecasts from four different mortality models. We consider one discrete-time model proposed by Lee and Carter (1992) and three continuous-time models: the Wills and Sherris (2011) model, the Feller process and the Ornstein-Uhlenbeck (OU) process. The first two models estimate the whole surface of mortality simultaneously, while in the latter two, each generation is modelled and calibrated separately. We calibrate the models to UK and Australian population data. We find that all the models show relatively similar absolute total error for a given dataset, except the Lee-Carter model, whose performance differs significantly. To evaluate the forecasting performance we therefore look at two alternative measures: the relative error between the forecasted and the actual mortality rates and the percentage of actual mortality rates which fall within a prediction interval. In terms of the prediction intervals, the results are more divergent since each model implies a different structure for the variance of mortality rates. According to our experiments, the Wills and Sherris model produces superior results in terms of the prediction intervals. However, in terms of the mean absolute error, the OU and the Feller processes perform better. The forecasting performance of the Lee Carter model is mostly dependent on the choice of the dataset. View Full-Text
Keywords: stochastic mortality; affine processes; survival probability modelling; mortality probability calibration stochastic mortality; affine processes; survival probability modelling; mortality probability calibration

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Novokreshchenova, A. Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models. Risks 2016, 4, 45.

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