LSTM-Based Coherent Mortality Forecasting for Developing Countries
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 necessa...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/12/2/27 |
| _version_ | 1797297048935989248 |
|---|---|
| author | Jose Garrido Yuxiang Shang Ran Xu |
| author_facet | Jose Garrido Yuxiang Shang Ran Xu |
| author_sort | Jose Garrido |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:14:38Z |
| format | Article |
| id | doaj.art-13e1cec4896d41f8bd4dc73c67905811 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-07T22:14:38Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-13e1cec4896d41f8bd4dc73c679058112024-02-23T15:33:20ZengMDPI AGRisks2227-90912024-02-011222710.3390/risks12020027LSTM-Based Coherent Mortality Forecasting for Developing CountriesJose Garrido0Yuxiang Shang1Ran Xu2Department of Mathematics and Statistics, Concordia University, Montreal, QC H3G 1M8, CanadaDepartment of Financial and Actuarial Mathematics, Xi’an Jiaotong–Liverpool University, Suzhou 215123, ChinaDepartment of Financial and Actuarial Mathematics, Xi’an Jiaotong–Liverpool University, Suzhou 215123, ChinaThis 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.https://www.mdpi.com/2227-9091/12/2/27coherent mortality forecastingLSTMdeveloping countrieslife expectancylifespan disparity |
| spellingShingle | Jose Garrido Yuxiang Shang Ran Xu LSTM-Based Coherent Mortality Forecasting for Developing Countries Risks coherent mortality forecasting LSTM developing countries life expectancy lifespan disparity |
| title | LSTM-Based Coherent Mortality Forecasting for Developing Countries |
| title_full | LSTM-Based Coherent Mortality Forecasting for Developing Countries |
| title_fullStr | LSTM-Based Coherent Mortality Forecasting for Developing Countries |
| title_full_unstemmed | LSTM-Based Coherent Mortality Forecasting for Developing Countries |
| title_short | LSTM-Based Coherent Mortality Forecasting for Developing Countries |
| title_sort | lstm based coherent mortality forecasting for developing countries |
| topic | coherent mortality forecasting LSTM developing countries life expectancy lifespan disparity |
| url | https://www.mdpi.com/2227-9091/12/2/27 |
| work_keys_str_mv | AT josegarrido lstmbasedcoherentmortalityforecastingfordevelopingcountries AT yuxiangshang lstmbasedcoherentmortalityforecastingfordevelopingcountries AT ranxu lstmbasedcoherentmortalityforecastingfordevelopingcountries |