A systematic vector autoregressive framework for modeling and forecasting mortality

Jackie Li, Jia Liu and Adam Butt

Journal of Forecasting, 2024, vol. 43, issue 6, 2279-2297

Abstract: Recently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They have been 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 generalized systematic vector autoregressive framework for modeling and forecasting mortality. Under this framework, we progressively increase the sophistication of the diagonal parameters in the autoregressive matrix and formulate a range of model structures in a systematic fashion. They offer much flexibility for capturing the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modelers, demographers, and industry practitioners. Using the mortality data of Australia, Japan, New Zealand, and Taiwan, we demonstrate that the proposed approach generates appropriate forecasts of mortality rates and life expectancies and produces very good performance in the fitting and out‐of‐sample analysis.

Date: 2024
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https://doi.org/10.1002/for.3127

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:6:p:2279-2297

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