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Forecasting
Special Issues
Mortality Modeling and Forecasting
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Mortality Modeling and Forecasting

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 17099

Special Issue Editors

Prof. Dr. Francesca Perla

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Guest Editor
Department of Management and Quantitative Sciences, Parthenope University of Naples, Via Generale Parisi n. 13, 80133 Naples, Italy
Interests: numerical analysis; machine learning; stochastic modeling; Solvency II; mortality modeling; parallel computing
Dr. Salvatore Scognamiglio

E-Mail
Guest Editor
Department of Management and Quantitative Sciences, Parthenope University of Naples, Via Generale Parisi n. 13, 80133 Naples, Italy
Interests: life insurance; machine learning; mortality modeling; time series; Solvency II

Special Issue Information

Dear Colleagues,

Mortality influences many aspects of our society such as pension plans, healthcare systems, and the insurance industry. The continuing increases in life expectancy beyond previously held limits have brought to the fore the critical importance of mortality forecasting. In the last several decades, the efforts of demographers, statisticians, and actuaries across the world have been devoted to better understanding the underlying patterns of mortality improvements and producing credible mortality projection. Different approaches and methods have been developed and investigated in the recent literature. Some prominent examples include (but are not limited to) factor-based models such as the Lee–Carter (1992) model and its extensions, time-series models, continuous-time models, machine-learning-based models, and the respective multi-population extensions. Despite these advances, more work is still needed.  

This Special Issue aims to collect innovative research papers on mortality forecasting methods and their potential applications. Comprehensive survey papers, as the basis for future research ideas, will also be considered. We also wish to encourage practitioners and young researchers to submit their research to us.

Prof. Dr. Francesca Perla
Dr. Salvatore Scognamiglio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mortality modeling
  • mortality forecasting
  • longevity risk
  • life expectancy
  • life insurance
  • population studies

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Published Papers (4 papers)

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Research

26 pages, 1345 KiB  
Article
Sex Differential Dynamics in Coherent Mortality Models
by Snorre Jallbjørn and Søren Fiig Jarner
Forecasting 2022, 4(4), 819-844; https://doi.org/10.3390/forecast4040045 - 29 Sep 2022
Cited by 1 | Viewed by 2273
Abstract
The main purpose of coherent mortality models is to produce plausible, joint forecasts for related populations avoiding, e.g., crossing or diverging mortality trajectories; however, the coherence assumption is very restrictive and it enforces trends that may be at odds with data. In this [...] Read more.
The main purpose of coherent mortality models is to produce plausible, joint forecasts for related populations avoiding, e.g., crossing or diverging mortality trajectories; however, the coherence assumption is very restrictive and it enforces trends that may be at odds with data. In this paper we focus on coherent, two-sex mortality models and we prove, under suitable conditions, that the coherence assumption implies sex gap unimodality, i.e., we prove that the difference in life expectancy between women and men will first increase and then decrease. Moreover, we demonstrate that, in the model, the sex gap typically peaks when female life expectancy is between 30 to 50 years. This explains why coherent mortality models predict narrowing sex gaps for essentially all Western European countries and all jump-off years since the 1950s, despite the fact that the actual sex gap was widening until the 1980s. In light of these findings, we discuss the current role of coherence as the gold standard for multi-population mortality models. Full article
(This article belongs to the Special Issue Mortality Modeling and Forecasting)
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15 pages, 809 KiB  
Article
Machine-Learning-Based Functional Time Series Forecasting: Application to Age-Specific Mortality Rates
by Ufuk Beyaztas and Hanlin Shang
Forecasting 2022, 4(1), 394-408; https://doi.org/10.3390/forecast4010022 - 18 Mar 2022
Cited by 4 | Viewed by 3697
Abstract
We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional principal components and their associated principal component scores. Machine-learning-based multi-step-ahead [...] Read more.
We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional principal components and their associated principal component scores. Machine-learning-based multi-step-ahead forecasting strategies, which automatically learn the underlying structure of the data, are used to obtain the future realization of the principal component scores. The forecasted mortality curves are obtained by combining the dynamic functional principal components and forecasted principal component scores. The point and interval forecast accuracy of the proposed method is evaluated using six age-specific mortality datasets and compared favorably with four existing functional time series methods. Full article
(This article belongs to the Special Issue Mortality Modeling and Forecasting)
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11 pages, 2838 KiB  
Article
Projecting Mortality Rates to Extreme Old Age with the CBDX Model
by Kevin Dowd and David Blake
Forecasting 2022, 4(1), 208-218; https://doi.org/10.3390/forecast4010012 - 2 Feb 2022
Viewed by 3114
Abstract
We introduce a simple extension to the CBDX model to project cohort mortality rates to extreme old age. The proposed approach fits a polynomial to a sample of age effects, uses the fitted polynomial to project the age effects to ages beyond the [...] Read more.
We introduce a simple extension to the CBDX model to project cohort mortality rates to extreme old age. The proposed approach fits a polynomial to a sample of age effects, uses the fitted polynomial to project the age effects to ages beyond the sample age range, then splices the sample and projected age effects, and uses the spliced age effects to obtain mortality rates for the higher ages. The proposed approach can be used to value financial instruments such as life annuities that depend on projections of extreme old age mortality rates. Full article
(This article belongs to the Special Issue Mortality Modeling and Forecasting)
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25 pages, 2145 KiB  
Article
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
by Thabang Mathonsi and Terence L. van Zyl
Forecasting 2022, 4(1), 1-25; https://doi.org/10.3390/forecast4010001 - 22 Dec 2021
Cited by 21 | Viewed by 6627
Abstract
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model [...] Read more.
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction. Full article
(This article belongs to the Special Issue Mortality Modeling and Forecasting)
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