Mortality Forecasting with Generalized Additive Mixed Models
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915619389046784 |
|---|---|
| author | Dastranj, Reza Kolar, Martin |
| author_facet | Dastranj, Reza Kolar, Martin |
| contents | This study introduces a novel generalized additive mixed model (GAMM) for mortality modelling, utilizing the mortality covariate $k_t$ as proposed by Dastranj-Kolar. Our findings indicate that the GAMM effectively addresses this shortcoming. Given that ASDRs constitute longitudinal data, as noted in the LME framework, the GAMM offers a more flexible and suitable approach for both modeling and forecasting mortality rates. Empirical evaluations using data from the Human Mortality Database (HMD) demonstrate the GAMM's strong ability to reproduce observed mortality patterns with high precision. Comparative analyses show that the GAMM consistently outperforms the LL model in both in-sample fit and out-of-sample forecasting across multiple populations. These results highlight the GAMM's potential as a robust and reliable tool for mortality modeling and long-term demographic forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18698 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mortality Forecasting with Generalized Additive Mixed Models Dastranj, Reza Kolar, Martin Applications This study introduces a novel generalized additive mixed model (GAMM) for mortality modelling, utilizing the mortality covariate $k_t$ as proposed by Dastranj-Kolar. Our findings indicate that the GAMM effectively addresses this shortcoming. Given that ASDRs constitute longitudinal data, as noted in the LME framework, the GAMM offers a more flexible and suitable approach for both modeling and forecasting mortality rates. Empirical evaluations using data from the Human Mortality Database (HMD) demonstrate the GAMM's strong ability to reproduce observed mortality patterns with high precision. Comparative analyses show that the GAMM consistently outperforms the LL model in both in-sample fit and out-of-sample forecasting across multiple populations. These results highlight the GAMM's potential as a robust and reliable tool for mortality modeling and long-term demographic forecasting. |
| title | Mortality Forecasting with Generalized Additive Mixed Models |
| topic | Applications |
| url | https://arxiv.org/abs/2311.18698 |