- -

A Comparison of Forecasting Mortality Models Using Resampling Methods

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A Comparison of Forecasting Mortality Models Using Resampling Methods

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Atance;Debón;Navarro ...
Tamaño: 1.894Mb
Formato: PDF
Descripción: Versión editorial
Abrir
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.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem