Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model
Abstract
:1. Introduction
- How can one handle differences in the mortality of multiple populations which make the assumption of a single common age effect implausible?
- How can one identify clusters of populations with similar age effects?
- What information can be obtained on similarities and dissimilarities between the age effects of the considered populations by applying different clustering algorithms?
- How do CAE-type models based on a cluster analysis of populations perform on different data sets compared to several benchmark models from the literature?
2. Methodological Preliminaries
2.1. Notation
2.2. Overview of Existing Models
2.3. Poisson Maximum Likelihood Estimation
2.4. The Bayesian Information Criterion
3. Clustering-Based Common Age Effect Models
3.1. k-Means Clustering
3.2. Augmented Common Factor Clustering
- (the ACF model fits well to the data), and
- (i) (the random walk fits well to the estimated population-specific period effect), or(ii) and (the AR(1) process fits well to the estimated population-specific period effect and is mean-reverting).
- (the population-specific factor significantly enhances the fit), or
- (the common factor model does not fit well enough on its own).
3.3. Likelihood-Ratio-Based Clustering
3.4. Fuzzy Maximum Likelihood Clustering
4. Empirical Model Comparison
4.1. Data
4.2. Clustering Results
4.3. Goodness of Fit
4.4. Forecasting Performance
- ,
- mean absolute error, ,
- mean absolute percentage error, ,
- root-mean-square error, ,
5. Conclusions
- Given the availability of data containing features other than age, country and sex such as socioeconomic or health-related characteristics, our clustering-based models could be extended to include such features as well.
- A change point test or similar methods could be applied in order to find the optimal training period instead of selecting it arbitrarily (see Sweeting 2011).
- To make the clustering-based models more parsimonious, populations could not only share the age effect parameters but also the average mortality level parameters , which Wen et al. (2020) have found to work well for the standard CAE model. Alternatively, using more parameters to improve the fit, we could also include cohort effects (see Renshaw and Haberman 2006) or more than one age-period interaction term (see Kleinow 2015). For the latter extension, one would need to decide on how exactly the clustering of the age effects is determined.
- We have not devoted much attention to the projection of the time series and mostly just used the standard approach, i.e., the random walk with drift. Of course, there are more sophisticated ways to project these time series, for example using general ARIMA models or imposing a non-trivial correlation structure, and thereby ensure coherence or semicoherence (Li et al. 2017) of the projections within the clusters or explicitly model dependencies between the clusters.
- In this regard, it would also be interesting to introduce our clustering algorithms to the locally coherent modeling framework of Guibert et al. (2020). More precisely, all of the clustering algorithms in this paper can potentially be extended to cluster period effects instead of age effects as well. Two aspects which should be addressed in this context are the increased importance of choosing a suitable projection method for the obtained cluster-specific period effects and the necessity of a new identifiability analysis for a fuzzy clustering model on the period effects.
- Our figures clearly show that the estimated age effects lack smoothness, which affects the resulting fitted and projected death rates and, even before that, also might have an undesirable influence on the obtained clustering. It could be beneficial to smooth the parameters, for example via a penalized log-likelihood approach (see Delwarde et al. 2007). In particular, it would be interesting how this influences the clustering results and how it changes the remaining parameters of the fuzzy maximum likelihood clustering model. Moreover, for the k-means method, other dimension reduction techniques such as PCA (see Debón et al. 2017) could be applied to the age effect vectors as well before performing the clustering.
- With regard to our clustering algorithms, we have found that the BIC as a model selection criterion may lead to suboptimal out-of-sample performance. Other methods for selecting the number of clusters or other hyperparameters of the clustering-based models such as cross validation or the criteria used in Debón et al. (2017) should be investigated, which might lead to a further improvement in the out-of-sample performance of these models as exemplified by the fuzzy maximum likelihood clustering model in Table 3.
- We emphasize once more that the k-means algorithm is only one of many possible clustering methods that can be applied to the framework of Section 3.1. It would be interesting to compare it to other techniques like DBSCAN, spectral clustering or—using a different distance measure—k-medians clustering. In particular, instead of k-means one could also apply the fuzzy c-means algorithm and compare the obtained results to the fuzzy maximum likelihood clustering approach we have proposed (see Hatzopoulos and Haberman 2013).
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Calculating the Number of Free Parameters
Appendix B. Details on the ACF Clustering Algorithm
Algorithm A1 The ACF fitting and clustering algorithm, see Section 3.2. |
Input: Death rates , explanation ratio threshold , improvement ratio threshold . Output: Number of clusters k, clustering function C, calibrated ACF model parameters and , time series processes for projecting and .
|
Appendix C. Hierarchical Clustering in the Likelihood-Ratio-Based Clustering Algorithm
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Cluster | k-Means | ACF-Based | Likelihood-Ratio-Based (AL) | Fuzzy ML Clustering () | Fuzzy ML Clustering () |
---|---|---|---|---|---|
1 | AUT | AUS, AUT, CAN, CHE, FRA, SWE, UK, USA | AUS | AUS, CAN, DNK, NZL, UK, USA | AUS, CAN, NZL, UK |
2 | CHE, FRA, SWE | DNK | AUT, DNK | AUT, CHE, FRA, SWE | AUT, FRA, SWE |
3 | AUS, CAN, NZL, UK, USA | NZL | CAN | - | CHE, USA |
4 | DNK | - | FRA, UK | - | DNK |
5 | - | - | NZL | - | - |
6 | - | - | SWE, USA | - | - |
7 | - | - | CHE | - | - |
Model | Lmax | npar | BIC |
---|---|---|---|
ACF (SVD) | — | 1299 | −75,684 |
CAE (cPCA) | — | 774 | −78,483 |
ILC (SVD) | — | 1080 | −77,550 |
ILC (MLE) | −86,791 | 1080 | 183,893 |
CAE (MLE) | −91,299 | 774 | 189,988 |
CAE, | |||
k-means | −87,584 | 876 | 183,532 |
CAE, | |||
ACF-based | −90,986 | 842 | 190,009 |
CAE, | |||
LR (av. linkage) | −88,233 | 978 | 185,803 |
CAE Fuzzy, | |||
−87,983 | 816 | 183,756 | |
CAE Fuzzy, | |||
(chosen by BIC) | −87,054 | 894 | 182,643 |
Model | Bias | MAE | MAPE | RMSE |
---|---|---|---|---|
ACF (SVD) | 5.92‰ | 6.77‰ | 20.91% | 9.65‰ |
CAE (cPCA) | 5.97‰ | 6.72‰ | 19.95% | 9.80‰ |
ILC (SVD) | 5.94‰ | 6.76‰ | 20.40% | 9.99‰ |
ILC (MLE) | 6.01‰ | 6.75‰ | 20.38% | 10.05‰ |
CAE (MLE) | 6.14‰ | 6.75‰ | 19.64% | 9.82‰ |
CAE, | ||||
k-means | 6.04‰ | 6.68‰ | 20.29% | 9.94‰ |
CAE, | ||||
ACF-based | 6.06‰ | 6.68‰ | 19.98% | 9.65‰ |
CAE, | ||||
LR (av. linkage) | 6.37‰ | 7.01‰ | 20.04% | 10.58‰ |
CAE Fuzzy, | ||||
5.90‰ | 6.47‰ | 19.63% | 9.43‰ | |
CAE Fuzzy, | ||||
(chosen by BIC) | 6.03‰ | 6.76‰ | 20.27% | 10.16‰ |
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Schnürch, S.; Kleinow, T.; Korn, R. Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model. Risks 2021, 9, 45. https://doi.org/10.3390/risks9030045
Schnürch S, Kleinow T, Korn R. Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model. Risks. 2021; 9(3):45. https://doi.org/10.3390/risks9030045
Chicago/Turabian StyleSchnürch, Simon, Torsten Kleinow, and Ralf Korn. 2021. "Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model" Risks 9, no. 3: 45. https://doi.org/10.3390/risks9030045
APA StyleSchnürch, S., Kleinow, T., & Korn, R. (2021). Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model. Risks, 9(3), 45. https://doi.org/10.3390/risks9030045