A Comparison of Forecasting Mortality Models Using Resampling Methods
Mostrar el registro sencillo del ítem
| dc.contributor.author | Atance, David
|
es_ES |
| dc.contributor.author | Debón Aucejo, Ana María
|
es_ES |
| dc.contributor.author | Navarro, Eliseo
|
es_ES |
| dc.date.accessioned | 2021-02-24T04:31:53Z | |
| dc.date.available | 2021-02-24T04:31:53Z | |
| dc.date.issued | 2020-09 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10251/162247 | |
| dc.description.abstract | [EN] The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies' profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee-Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area. | es_ES |
| dc.description.sponsorship | The research of David Atance was supported by a grant (Contrato Predoctoral de Formacion Universitario) from the University of Alcala. This work is partially supported by a grant from the MEIyC (Ministerio de Economia, Industria y Competitividad, Spain project ECO2017-89715-P). | es_ES |
| dc.language | Inglés | es_ES |
| dc.publisher | MDPI AG | es_ES |
| dc.relation.ispartof | Mathematics | es_ES |
| dc.rights | Reconocimiento (by) | es_ES |
| dc.subject | Forecasting | es_ES |
| dc.subject | Lee¿Carter model | es_ES |
| dc.subject | Resampling methods | es_ES |
| dc.subject | Cross-validation | es_ES |
| dc.subject | Cobweb graph | es_ES |
| dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
| dc.title | A Comparison of Forecasting Mortality Models Using Resampling Methods | es_ES |
| dc.type | Artículo | es_ES |
| dc.identifier.doi | 10.3390/math8091550 | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2017-89715-P/ES/ANALISIS DEL RIESGO EN LOS MERCADOS FINANCIEROS/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//MTM2013-45381-P/ES/DIFERENCIAS DE LONGEVIDAD EN LA UNION EUROPEA: APLICACION DE NUEVOS METODOS PARA SU EVALUACION Y ANALISIS/ | es_ES |
| dc.rights.accessRights | Abierto | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
| dc.description.bibliographicCitation | Atance, D.; Debón Aucejo, AM.; Navarro, E. (2020). A Comparison of Forecasting Mortality Models Using Resampling Methods. Mathematics. 8(9):1-21. https://doi.org/10.3390/math8091550 | es_ES |
| dc.description.accrualMethod | S | es_ES |
| dc.relation.publisherversion | https://doi.org/10.3390/math8091550 | es_ES |
| dc.description.upvformatpinicio | 1 | es_ES |
| dc.description.upvformatpfin | 21 | es_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.description.volume | 8 | es_ES |
| dc.description.issue | 9 | es_ES |
| dc.identifier.eissn | 2227-7390 | es_ES |
| dc.relation.pasarela | S\418163 | es_ES |
| dc.contributor.funder | Universidad de Alcalá | es_ES |
| dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
| dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
| dc.description.references | BOOTH, H., MAINDONALD, J., & SMITH, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325-336. doi:10.1080/00324720215935 | es_ES |
| dc.description.references | Brouhns, N., Denuit, M., & Vermunt, J. K. (2002). A Poisson log-bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31(3), 373-393. doi:10.1016/s0167-6687(02)00185-3 | es_ES |
| dc.description.references | Lee, R., & Miller, T. (2001). Evaluating the performance of the lee-carter method for forecasting mortality. Demography, 38(4), 537-549. doi:10.1353/dem.2001.0036 | es_ES |
| dc.description.references | Cairns, A. J. G., Blake, D., & Dowd, K. (2006). A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration. Journal of Risk & Insurance, 73(4), 687-718. doi:10.1111/j.1539-6975.2006.00195.x | es_ES |
| dc.description.references | Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., Epstein, D., Ong, A., & Balevich, I. (2009). A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States. North American Actuarial Journal, 13(1), 1-35. doi:10.1080/10920277.2009.10597538 | es_ES |
| dc.description.references | Renshaw, A. E., & Haberman, S. (2003). Lee–Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255-272. doi:10.1016/s0167-6687(03)00138-0 | es_ES |
| dc.description.references | Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38(3), 556-570. doi:10.1016/j.insmatheco.2005.12.001 | es_ES |
| dc.description.references | Hainaut, D. (2018). A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST. ASTIN Bulletin, 48(02), 481-508. doi:10.1017/asb.2017.45 | es_ES |
| dc.description.references | Levantesi, S., & Pizzorusso, V. (2019). Application of Machine Learning to Mortality Modeling and Forecasting. Risks, 7(1), 26. doi:10.3390/risks7010026 | es_ES |
| dc.description.references | Pascariu, M. D., Lenart, A., & Canudas-Romo, V. (2019). The maximum entropy mortality model: forecasting mortality using statistical moments. Scandinavian Actuarial Journal, 2019(8), 661-685. doi:10.1080/03461238.2019.1596974 | es_ES |
| dc.description.references | S̀liwka, P., & Socha, L. (2018). A proposition of generalized stochastic Milevsky–Promislov mortality models. Scandinavian Actuarial Journal, 2018(8), 706-726. doi:10.1080/03461238.2018.1431805 | es_ES |
| dc.description.references | Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., & Elith, J. (2018). A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sensing of Environment, 208, 145-153. doi:10.1016/j.rse.2018.02.026 | es_ES |
| dc.description.references | Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21(15), 3301-3307. doi:10.1093/bioinformatics/bti499 | es_ES |
| dc.description.references | Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4(none). doi:10.1214/09-ss054 | es_ES |
| dc.description.references | Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. doi:10.1111/j.2517-6161.1974.tb00994.x | es_ES |
| dc.description.references | Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70-83. doi:10.1016/j.csda.2017.11.003 | es_ES |
| dc.description.references | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1). doi:10.1214/aos/1176344552 | es_ES |
| dc.description.references | Brouhns, N., Denuit *, M., & Van Keilegom, I. (2005). Bootstrapping the Poisson log-bilinear model for mortality forecasting. Scandinavian Actuarial Journal, 2005(3), 212-224. doi:10.1080/03461230510009754 | es_ES |
| dc.description.references | D’Amato, V., Haberman, S., Piscopo, G., & Russolillo, M. (2012). Modelling dependent data for longevity projections. Insurance: Mathematics and Economics, 51(3), 694-701. doi:10.1016/j.insmatheco.2012.09.008 | es_ES |
| dc.description.references | Debón, A., Martínez-Ruiz, F., & Montes, F. (2012). Temporal Evolution of Mortality Indicators. North American Actuarial Journal, 16(3), 364-377. doi:10.1080/10920277.2012.10590647 | es_ES |
| dc.description.references | Debón, A., Montes, F., Mateu, J., Porcu, E., & Bevilacqua, M. (2008). Modelling residuals dependence in dynamic life tables: A geostatistical approach. Computational Statistics & Data Analysis, 52(6), 3128-3147. doi:10.1016/j.csda.2007.08.006 | es_ES |
| dc.description.references | Koissi, M.-C., Shapiro, A. F., & Högnäs, G. (2006). Evaluating and extending the Lee–Carter model for mortality forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38(1), 1-20. doi:10.1016/j.insmatheco.2005.06.008 | es_ES |
| dc.description.references | Liu, X., & Braun, W. J. (2010). Investigating Mortality Uncertainty Using the Block Bootstrap. Journal of Probability and Statistics, 2010, 1-15. doi:10.1155/2010/813583 | es_ES |
| dc.description.references | Härdle, W., Horowitz, J., & Kreiss, J. (2003). Bootstrap Methods for Time Series. International Statistical Review, 71(2), 435-459. doi:10.1111/j.1751-5823.2003.tb00485.x | es_ES |
| dc.description.references | Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. doi:10.1016/j.ins.2011.12.028 | es_ES |
| dc.description.references | Booth, H., Hyndman, R. J., Tickle, L., & de Jong, P. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research, 15, 289-310. doi:10.4054/demres.2006.15.9 | es_ES |
| dc.description.references | Delwarde, A., Denuit, M., & Eilers, P. (2007). Smoothing the Lee–Carter and Poisson log-bilinear models for mortality forecasting. Statistical Modelling, 7(1), 29-48. doi:10.1177/1471082x0600700103 | es_ES |
| dc.description.references | Debón, A., Montes, F., & Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operational Research, 189(3), 624-637. doi:10.1016/j.ejor.2006.07.050 | es_ES |
| dc.description.references | Currie, I. D., Durban, M., & Eilers, P. H. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4(4), 279-298. doi:10.1191/1471082x04st080oa | es_ES |
| dc.description.references | Chen, K., Liao, J., Shang, X., & Li, J. S.-H. (2009). «A Quantitative Comparison of Stochastic Mortality Models Using Data from England and Wales and the United States,» Andrew J. G. Cairns, David Blake, Kevin Dowd, Guy D. Coughlan, David Epstein, Alen Ong, and Igor Balevich, Vol. 13, No. 1, 2009. North American Actuarial Journal, 13(4), 514-520. doi:10.1080/10920277.2009.10597572 | es_ES |
| dc.description.references | Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45(3), 393-404. doi:10.1016/j.insmatheco.2009.08.006 | es_ES |
| dc.description.references | Debón, A., Martínez-Ruiz, F., & Montes, F. (2010). A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327-336. doi:10.1016/j.insmatheco.2010.07.007 | es_ES |
| dc.description.references | Yang, S. S., Yue, J. C., & Huang, H.-C. (2010). Modeling longevity risks using a principal component approach: A comparison with existing stochastic mortality models. Insurance: Mathematics and Economics, 46(1), 254-270. doi:10.1016/j.insmatheco.2009.09.013 | es_ES |
| dc.description.references | Haberman, S., & Renshaw, A. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35-55. doi:10.1016/j.insmatheco.2010.09.003 | es_ES |
| dc.description.references | Mitchell, D., Brockett, P., Mendoza-Arriaga, R., & Muthuraman, K. (2013). Modeling and forecasting mortality rates. Insurance: Mathematics and Economics, 52(2), 275-285. doi:10.1016/j.insmatheco.2013.01.002 | es_ES |
| dc.description.references | Danesi, I. L., Haberman, S., & Millossovich, P. (2015). Forecasting mortality in subpopulations using Lee–Carter type models: A comparison. Insurance: Mathematics and Economics, 62, 151-161. doi:10.1016/j.insmatheco.2015.03.010 | es_ES |
| dc.description.references | Yang, B., Li, J., & Balasooriya, U. (2014). Cohort extensions of the Poisson common factor model for modelling both genders jointly. Scandinavian Actuarial Journal, 2016(2), 93-112. doi:10.1080/03461238.2014.908411 | es_ES |
| dc.description.references | Neves, C., Fernandes, C., & Hoeltgebaum, H. (2017). Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models. Insurance: Mathematics and Economics, 75, 48-57. doi:10.1016/j.insmatheco.2017.04.004 | es_ES |
| dc.description.references | University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany)www.mortality.org | es_ES |
| dc.description.references | Hunt, A., & Blake, D. P. (2015). Identifiability in Age/Period/Cohort Mortality Models. SSRN Electronic Journal. doi:10.2139/ssrn.3552213 | es_ES |
| dc.description.references | Generalized Nonlinear Models in R: An Overview of the Gnm Packagehttps://cran.r-project.org/package=gnm | es_ES |
| dc.description.references | Lachenbruch, P. A., & Mickey, M. R. (1968). Estimation of Error Rates in Discriminant Analysis. Technometrics, 10(1), 1-11. doi:10.1080/00401706.1968.10490530 | es_ES |
| dc.description.references | Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting, 16(4), 437-450. doi:10.1016/s0169-2070(00)00065-0 | es_ES |
| dc.description.references | Diaz, G., Debón, A., & Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus, 74(1). doi:10.1186/s41118-018-0038-6 | es_ES |
| dc.description.references | Ahcan, A., Medved, D., Olivieri, A., & Pitacco, E. (2014). Forecasting mortality for small populations by mixing mortality data. Insurance: Mathematics and Economics, 54, 12-27. doi:10.1016/j.insmatheco.2013.10.013 | es_ES |
| dc.description.references | FORSYTHE, A., & HARTICAN, J. A. (1970). Efficiency of confidence intervals generated by repeated subsample calculations. Biometrika, 57(3), 629-639. doi:10.1093/biomet/57.3.629 | es_ES |
| dc.description.references | BURMAN, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3), 503-514. doi:10.1093/biomet/76.3.503 | es_ES |
| dc.description.references | Shao, J. (1993). Linear Model Selection by Cross-validation. Journal of the American Statistical Association, 88(422), 486-494. doi:10.1080/01621459.1993.10476299 | es_ES |
| dc.description.references | Li, H., & O’Hare, C. (2019). Mortality Forecasting: How Far Back Should We Look in Time? Risks, 7(1), 22. doi:10.3390/risks7010022 | es_ES |
| dc.description.references | Breiman, L., & Spector, P. (1992). Submodel Selection and Evaluation in Regression. The X-Random Case. International Statistical Review / Revue Internationale de Statistique, 60(3), 291. doi:10.2307/1403680 | es_ES |
| dc.description.references | Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705 | es_ES |
| dc.description.references | Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2). doi:10.1214/aos/1176344136 | es_ES |
| dc.description.references | Hunt, A., & Blake, D. (2014). A General Procedure for Constructing Mortality Models. North American Actuarial Journal, 18(1), 116-138. doi:10.1080/10920277.2013.852963 | es_ES |
| dc.description.references | Moritz, S., & Bartz-Beielstein, T. (2017). imputeTS: Time Series Missing Value Imputation in R. The R Journal, 9(1), 207. doi:10.32614/rj-2017-009 | es_ES |
| dc.description.references | Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social Relationships and Mortality Risk: A Meta-analytic Review. PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.1000316 | es_ES |
| dc.relation.references | 10.1080/00324720215935 | es_ES |
| dc.relation.references | 10.1016/S0167-6687(02)00185-3 | es_ES |
| dc.relation.references | 10.1353/dem.2001.0036 | es_ES |
| dc.relation.references | 10.1111/j.1539-6975.2006.00195.x | es_ES |
| dc.relation.references | 10.1080/10920277.2009.10597538 | es_ES |
| dc.relation.references | 10.1016/S0167-6687(03)00138-0 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2005.12.001 | es_ES |
| dc.relation.references | 10.1017/asb.2017.45 | es_ES |
| dc.relation.references | 10.3390/risks7010026 | es_ES |
| dc.relation.references | 10.1080/03461238.2019.1596974 | es_ES |
| dc.relation.references | 10.1080/03461238.2018.1431805 | es_ES |
| dc.relation.references | 10.1016/j.rse.2018.02.026 | es_ES |
| dc.relation.references | 10.1093/bioinformatics/bti499 | es_ES |
| dc.relation.references | 10.1214/09-SS054 | es_ES |
| dc.relation.references | 10.1111/j.2517-6161.1974.tb00994.x | es_ES |
| dc.relation.references | 10.1016/j.csda.2017.11.003 | es_ES |
| dc.relation.references | 10.1214/aos/1176344552 | es_ES |
| dc.relation.references | 10.1080/03461230510009754 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2012.09.008 | es_ES |
| dc.relation.references | 10.1080/10920277.2012.10590647 | es_ES |
| dc.relation.references | 10.1016/j.csda.2007.08.006 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2005.06.008 | es_ES |
| dc.relation.references | 10.1155/2010/813583 | es_ES |
| dc.relation.references | 10.1111/j.1751-5823.2003.tb00485.x | es_ES |
| dc.relation.references | 10.1016/j.ins.2011.12.028 | es_ES |
| dc.relation.references | 10.4054/DemRes.2006.15.9 | es_ES |
| dc.relation.references | 10.1177/1471082X0600700103 | es_ES |
| dc.relation.references | 10.1016/j.ejor.2006.07.050 | es_ES |
| dc.relation.references | 10.1191/1471082X04st080oa | es_ES |
| dc.relation.references | 10.1080/10920277.2009.10597572 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2009.08.006 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2010.07.007 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2009.09.013 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2010.09.003 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2013.01.002 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2015.03.010 | es_ES |
| dc.relation.references | 10.1080/03461238.2014.908411 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2017.04.004 | es_ES |
| dc.relation.references | 10.2139/ssrn.3552213 | es_ES |
| dc.relation.references | 10.1080/00401706.1968.10490530 | es_ES |
| dc.relation.references | 10.1016/S0169-2070(00)00065-0 | es_ES |
| dc.relation.references | 10.1186/s41118-018-0038-6 | es_ES |
| dc.relation.references | 10.1016/j.insmatheco.2013.10.013 | es_ES |
| dc.relation.references | 10.1093/biomet/57.3.629 | es_ES |
| dc.relation.references | 10.1093/biomet/76.3.503 | es_ES |
| dc.relation.references | 10.1080/01621459.1993.10476299 | es_ES |
| dc.relation.references | 10.3390/risks7010022 | es_ES |
| dc.relation.references | 10.2307/1403680 | es_ES |
| dc.relation.references | 10.1109/TAC.1974.1100705 | es_ES |
| dc.relation.references | 10.1214/aos/1176344136 | es_ES |
| dc.relation.references | 10.1080/10920277.2013.852963 | es_ES |
| dc.relation.references | 10.32614/RJ-2017-009 | es_ES |
| dc.relation.references | 10.1371/journal.pmed.1000316 | es_ES |
| dc.subject.ods | 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades | es_ES |
Ficheros en el ítem
