Browse > Article
http://dx.doi.org/10.29220/CSAM.2021.28.3.233

Comparison of accuracy between LC model and 4-PFM when COVID-19 impacts mortality structure  

Choi, Janghoon (Korea Insurance Research Institute)
Publication Information
Communications for Statistical Applications and Methods / v.28, no.3, 2021 , pp. 233-250 More about this Journal
Abstract
This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition
Keywords
Lee-Carter model; 4-parametric factor model; accuracy; mortality structure; reliable;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Rogers A and Little JS (1994). Parameterizing age patterns of demographic rates with the multiexponential model schedule, Mathematical Population Studies, 4, 175-195.   DOI
2 Chapman S, Alpers P, and Jones M (2016). Association between gun law and intentional firearm deaths in Australia, 1979-2013. Journal of the American Medical Association, 316, 291-299.   DOI
3 Currie L (2013). Smoothing constrained generalized linear models with an application to the LeeCarter model, Statistical Modelling, 13, 69-93.   DOI
4 Gompertz B (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Philosophical Transactions of the Royal Society of London, 115, 513-583.   DOI
5 Heligman L and Pollard JH (1980). The age pattern of mortality, Journal of the Institute of Actuaries, 107, 49-80.   DOI
6 Hunt A and Villegas AM (2015). Robustness and convergence in the Lee-Carter model with cohort effects, Insurance: Mathematics and Economics, 64, 186-202.   DOI
7 Kang JC, Lee DS, and Sung JH (2006). A study on the methods for forecasting mortality considering longevity risk, The Journal of Risk Management, 17, 153-178.
8 Kim NS (2020). Current situation and task of COVID-19, ISSUE & FOCUS, Korea Institute for Health and Social Affrairs, ISSN 2092-7117.
9 Lee HS, Baek CR, and Kim JH (2016). A modified Lee-Carter model based on the projection of the skewness of the mortality, Korean Journal of Applied Statistics, 29, 41-59.   DOI
10 Lee RD and Carter LR (1992). Modeling and forecasting U.S. mortality, Journal of the American Statistical Association, 87, 659-671.   DOI
11 Li H and Li J (2017). Optimizing the Lee-Carter approach in the presence of structural changes in time and age patterns of mortality improvements, Demography, 54, 1073-1095.   DOI
12 Li J, Chan WS, and Cheung SH (2012). Structural changes in the Lee-Carter mortality indexes: detection and implications, North American Actuarial Journal, 15, 13-31.   DOI
13 Li N and Lee R (2005). Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method, Demography, 42, 575-594.   DOI
14 Park YS, Jang SW, and Kim SY (2013). VECM-LC model for forecasting mortality in Korea, Survey Research, 14, 19-47.
15 Ukert B, Andreyeva E, and Branas CC (2017). Time series robustness checks to test the effects of the 1996 Australian firearm law on cause-specific mortality, Journal of Experimental Criminology.
16 Pfaff B (2008). VAR, SVAR and SVEC models: implementation within R package vars, Journal of Statistical Software, 27, 1-32.   DOI
17 Renshaw AE and Haberman S (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors, Insurance: Mathematics and Economics, 38, 556-570.   DOI
18 Statistics Korea (2020). Life-table, Retrieved from August 30, 2020, https://kosis.kr/statisticsList/statisticsListIndex.do?menuId=M0101&vwcd=MTZTITLE&parmTabId=M01 01#SelectSt atsBoxDiv.
19 Haldrup N and Rosenskjold PT (2019). A parametric factor model of the term structure of mortality, Econometrics, Retrieved from July 9th,2019,doi:10.3390/econometrics7010009.   DOI
20 Nigri A, Levantesi S, Marino M, Scognamiglio S, and Perla F (2019). A deep learning integrated Lee-Carter model, Risks, 7.
21 Promislow EL(2020). A geroscience perspective on COVID-19 mortality, The Journals of Gerontology.
22 Siler W (1979). A competing-risk model for animal mortality, Ecology, 60, 750-757.   DOI
23 Wisniowski A, Smith WF, Bijak J, Raymer J, and Forster JJ (2015), Bayesian population forecasting:extending the Lee Carter method, Demography, 52, 1035-1059.   DOI