A multivariate evolutionary credibility model for mortality improvement rates |
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| Institution: | 1. DFG Research Training Group 1100, University of Ulm, Ulm, Germany;2. Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;3. Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom;1. Department of Neurosurgery, Fujita Health University, Toyoake, Japan;2. Department of Radiology, Fujita Health University, Toyoake, Japan;3. Department of Pathology, Fujita Health University, Toyoake, Japan;1. Institute of Insurance Science, University of Ulm, Ulm, Germany;2. Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;3. Actuarial Research Group, AFI, Faculty of Business and Economics, Katholieke Universiteit Leuven, Leuven, Belgium;4. Department of Mathematical Statistics and Actuarial Science, University of the Freestate, Bloemfontein, South Africa |
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| Abstract: | The present paper proposes an evolutionary credibility model that describes the joint dynamics of mortality through time in several populations. Instead of modeling the mortality rate levels, the time series of population-specific mortality rate changes, or mortality improvement rates are considered and expressed in terms of correlated time factors, up to an error term. Dynamic random effects ensure the necessary smoothing across time, as well as the learning effect. They also serve to stabilize successive mortality projection outputs, avoiding dramatic changes from one year to the next. Statistical inference is based on maximum likelihood, properly recognizing the random, hidden nature of underlying time factors. Empirical illustrations demonstrate the practical interest of the approach proposed in the present paper. |
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| Keywords: | Mortality projection Predictive distribution Multi-population modeling ARMA process Lee-Carter model |
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