LSTM-Based Coherent Mortality Forecasting for Developing Countries

Jose Garrido, Yuxiang Shang and Ran Xu ()
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Jose Garrido: Department of Mathematics and Statistics, Concordia University, Montreal, QC H3G 1M8, Canada
Yuxiang Shang: Department of Financial and Actuarial Mathematics, Xi’an Jiaotong–Liverpool University, Suzhou 215123, China
Ran Xu: Department of Financial and Actuarial Mathematics, Xi’an Jiaotong–Liverpool University, Suzhou 215123, China

Risks, 2024, vol. 12, issue 2, 1-24

Abstract: This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries or regions. Many of such developing countries have experienced a rapid mortality decline over the past few decades. However, their recent mortality development trend is not necessarily driven by the same factors as their long-term behavior. Hence, we propose a time-varying mortality forecasting model based on the life expectancy and lifespan disparity gap between these developing countries and a selected benchmark group. Here, the mortality improvement trend for developing countries is expected to converge gradually to that of the benchmark group during the projection phase. More specifically, we use a unified deep neural network model with LSTM architecture to project the life expectancy and lifespan disparity difference, which further controls the rotation of the time-varying weight parameters in the model. This approach is applied to three developing countries and three developing regions. The empirical results show that this LSTM-based coherent forecasting method outperforms classical methods, especially for the long-term projections of mortality rates in developing countries.

Keywords: coherent mortality forecasting; LSTM; developing countries; life expectancy; lifespan disparity (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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