大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

A systematic vector autoregressive framework for modelling and forecasting mortality

Jackie Li (Monash University)

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4. A systematic vector autoregressive framework for modelling and forecasting mortality

大阪大学 数理・データ科学セミナー 金融・保険セミナーシリーズ 第142回

A systematic vector autoregressive framework for modelling and forecasting mortality

Jackie Li (Monash University)

Recently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They are shown to be able to overcome some of the limitations of the more traditional factor models such as the Lee-Carter model. In this paper, we propose a more generalised systematic vector autoregressive framework for modelling and forecasting mortality. Under this framework, we incorporate subdiagonals and / or superdiagonals of parameters progressively into the autoregressive matrix to formulate a range of model structures in a systematic fashion. They offer much flexibility for coping with the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modellers, demographers, and industry practitioners. Using the mortality data of England and Wales, France, Japan, and US, we demonstrate that the proposed approach produces decent fitting and forecasting performances.