Enhancing Mortality Forecasting through Bivariate Model–Based Ensemble

References

• Brainerd, E., and D. M Cutler. 2005. Autopsy on an empire: Understanding mortality in Russia and the former Soviet Union. Journal of Economic Perspectives 19 (1):107–30.
   Web of Science ®Google Scholar
• Bühlmann, P., and B. Yu. 2002. Analyzing bagging. Annals of Statistics 30 (4):927–61.
   Web of Science ®Google Scholar
• Cairns, A. J. G., D. Blake, and K. Dowd. 2006. A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration. Journal of Risk and Insurance 73 (4):687–718. doi:10.1111/j.1539-6975.2006.00195.x
   Web of Science ®Google Scholar
• Cairns, A. J. G., D. Blake, K. Dowd, G. D. Coughlan, D. Epstein, and M. Khalaf-Allah. 2011. Mortality density forecasts: An analysis of six stochastic mortality models. Insurance: Mathematics and Economics 48 (3):355–67. doi:10.1016/j.insmatheco.2010.12.005
   Web of Science ®Google Scholar
• Cairns, A. J. G., D. Blake, K. Dowd, G. D. Coughlan, D. Epstein, A. Ong, and I. Balevich. 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
   Google Scholar
• Chang, L., and Y. Shi. 2022. Forecasting mortality rates with a coherent ensemble averaging approach. ASTIN Bulletin 2022:1–27. doi:10.1017/asb.2022.23
   Web of Science ®Google Scholar
• Charrad, M., N. Ghazzali, V. Boiteau, and A. Niknafs. 2014. NbClust: an R package for determining the relevant number of clusters in a data set. Journal of statistical software 61:1–36.
   Google Scholar
• Claeskens, G., and N. L. Hjort. 2008. Model selection and model averaging. Cambridge: Cambridge University Press.
   Google Scholar
• Cox, M. A. A., and T. F. Cox. 2008. Multidimensional scaling. In Handbook of data visualization, eds. C.-H. Chen, W. Hardle and A. Unwin, 315–47. Berlin: Springer.
   Google Scholar
• DeMiguel, V., L. Garlappi, and R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/n portfolio strategy? Review of Financial Studies 22 (5):1915–953. doi:10.1093/rfs/hhm075
   Web of Science ®Google Scholar
• Diao, L., Y. Meng, and C. Weng. 2021. A DSA algorithm for mortality forecasting. North American Actuarial Journal 25 (3):438–58. doi:10.1080/10920277.2020.1806884
   Web of Science ®Google Scholar
• Diebold, F. X., and R. S. Mariano. 2002. Comparing predictive accuracy. Journal of Business and Economic Statistics 20 (1):134–44. doi:10.1198/073500102753410444
   Web of Science ®Google Scholar
• Dowd, K., A. J. G. Cairns, D. Blake, G. D. Coughlan, and M. Khalaf-Allah. 2011. A gravity model of mortality rates for two related populations. North American Actuarial Journal 15 (2):334–56. doi:10.1080/10920277.2011.10597624
   Google Scholar
• Hansen, B. E., and J. S. Racine. 2012. Jackknife model averaging. Journal of Econometrics 167 (1):38–46. doi:10.1016/j.jeconom.2011.06.019
   Web of Science ®Google Scholar
• Harvey, D., S. Leybourne, and P. Newbold. 1997. Testing the equality of prediction mean squared errors. International Journal of Forecasting 13 (2):281–91. doi:10.1016/S0169-2070(96)00719-4
   Web of Science ®Google Scholar
• Hatzopoulos, P., and S. Haberman. 2013. Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data. Insurance: Mathematics and Economics 52 (2):320–37. doi:10.1016/j.insmatheco.2012.12.009
   Web of Science ®Google Scholar
• Hunt, A., and D. Blake. 2021. On the structure and classification of mortality models. North American Actuarial Journal 25 (suppl. 1):S215–S34. doi:10.1080/10920277.2019.1649156
   Google Scholar
• Hyndman, R. J., and Y. Khandakar. 2007. Automatic time series for forecasting: the forecast package for R (No. 6/07). Monash University, Department of Econometrics and Business Statistics.
   Google Scholar
• Hyndman, R. J., H. Booth, and F. Yasmeen. 2013. Coherent mortality forecasting: The product-ratio method with functional time series models. Demography 50 (1):261–83. doi:10.1007/s13524-012-0145-5
   PubMed Web of Science ®Google Scholar
• Janssen, F., L. J. G. van Wissen, and A. E. Kunst. 2013. Including the smoking epidemic in internationally coherent mortality projections. Demography 50 (4):1341–362. doi:10.1007/s13524-012-0185-x
   PubMed Web of Science ®Google Scholar
• Kessy, S. R., M. Sherris, A. M. Villegas, and J. Ziveyi. 2022. Mortality forecasting using stacked regression ensembles. Scandinavian Actuarial Journal 2022 (7):591–626.
   Web of Science ®Google Scholar
• Kleinow, T. 2015. A common age effect model for the mortality of multiple populations. Insurance: Mathematics and Economics 63:147–52. doi:10.1016/j.insmatheco.2015.03.023
   Web of Science ®Google Scholar
• Lee, R. D., and L. R. Carter. 1992. Modeling and forecasting U.S. mortality. Journal of the American Statistical Association 87 (419):659–71. doi:10.1080/01621459.1992.10475265
   Web of Science ®Google Scholar
• Li, H., C. O’Hare, and F. Vahid. 2016. Two-dimensional kernel smoothing of mortality surface: An evaluation of cohort strength. Journal of Forecasting 35 (6):553–63. doi:10.1002/for.2399
   Web of Science ®Google Scholar
• Li, J. 2013. A Poisson common factor model for projecting mortality and life expectancy jointly for females and males. Population Studies 67 (1):111–26. doi:10.1080/00324728.2012.689316
   PubMed Web of Science ®Google Scholar
• Li, J. S.-H., W.-S. Chan, and R. Zhou. 2017. Semicoherent multipopulation mortality modeling: The impact on longevity risk securitization. Journal of Risk and Insurance 84 (3):1025–65. doi:10.1111/jori.12135
   Web of Science ®Google Scholar
• Li, J. S.-H., and M. R. Hardy. 2011. Measuring basis risk in longevity hedges. North American Actuarial Journal 15 (2):177–200. doi:10.1080/10920277.2011.10597616
   Google Scholar
• Li, J. S.-H., R. Zhou, and M. Hardy. 2015. A step-by-step guide to building two-population stochastic mortality models. Insurance: Mathematics and Economics 63:121–34. doi:10.1016/j.insmatheco.2015.03.021
   Web of Science ®Google Scholar
• Li, N., and R. Lee. 2005. Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography 42 (3):575–94. doi:10.1353/dem.2005.0021
   PubMed Web of Science ®Google Scholar
• Milidonis, A., and M. Efthymiou. 2017. Mortality leads and lags. Journal of Risk and Insurance 84 (suppl. 1):495–514. doi:10.1111/jori.12187
   Google Scholar
• Plat, R. 2009. On stochastic mortality modeling. Insurance: Mathematics and Economics 45 (3):393–404.
   Web of Science ®Google Scholar
• Raftery, A. E., D. Madigan, and J. A. Hoeting. 1997. Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92 (437): 179–91. doi:10.1080/01621459.1997.10473615
   Web of Science ®Google Scholar
• Renshaw, A. E., and S. Haberman. 2006. A cohort-based extension to the Lee-Carter model for mortality reduction factors. Insurance: Mathematics and Economics 38 (3):556–70. doi:10.1016/j.insmatheco.2005.12.001
   Web of Science ®Google Scholar
• Richman, R., and M. V. Wüthrich. 2021. A neural network extension of the Lee-Carter model to multiple populations. Annals of Actuarial Science 15 (2):346–66. doi:10.1017/S1748499519000071
   Web of Science ®Google Scholar
• Russolillo, M., G. Giordano, and S. Haberman. 2011. Extending the Lee-Carter model: A three-way decomposition. Scandinavian Actuarial Journal 2011 (2):96–117. doi:10.1080/03461231003611933
   Web of Science ®Google Scholar
• Shair, S., S. Purcal, and N. Parr. 2017. Evaluating extensions to coherent mortality forecasting models. Risks 5 (1):16. 10.3390/risks5010016
   Google Scholar
• Shang, H. L. 2012. Point and interval forecasts of age-specific life expectancies: A model averaging approach. Demographic Research 27:593–644. doi:10.4054/DemRes.2012.27.21
   Web of Science ®Google Scholar
• Shang, H. L. 2016. Mortality and life expectancy forecasting for a group of populations in developed countries: A multilevel functional data method. Annals of Applied Statistics 10 (3):1639–672.
   Web of Science ®Google Scholar
• Shang, H. L., and H. Booth. 2020. Synergy in fertility forecasting: Improving forecast accuracy through model averaging. Genus 76 (1):1–23. doi:10.1186/s41118-020-00099-y
   Google Scholar
• Shang, H. L., and S. Haberman. 2018. Model confidence sets and forecast combination: An application to age-specific mortality. Genus 74 (1):1–23. doi:10.1186/s41118-018-0043-9
   PubMedGoogle Scholar
• Shang, H. L., and R. J. Hyndman. 2017. Grouped functional time series forecasting: An application to age-specific mortality rates. Journal of Computational and Graphical Statistics 26 (2):330–43. doi:10.1080/10618600.2016.1237877
   Web of Science ®Google Scholar
• Tsai, C. C.-L., and E. S. Cheng. 2021. Incorporating statistical clustering methods into mortality models to improve forecasting performances. Insurance: Mathematics and Economics 99:42–62. doi:10.1016/j.insmatheco.2021.03.005
   Web of Science ®Google Scholar
• Wang, C.-W., and S. S. Yang. 2013. Pricing survivor derivatives with cohort mortality dependence under the Lee-Carter framework. Journal of Risk and Insurance 80 (4):1027–56. doi:10.1111/j.1539-6975.2012.01488.x
   Web of Science ®Google Scholar
• Yang, B., J. Li, and U. Balasooriya. 2016. 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
   Web of Science ®Google Scholar
• Zhou, R., Y. Wang, K. Kaufhold, J. S.-H. Li, and K. S. Tan. 2014. Modeling period effects in multi-population mortality models: Applications to Solvency II. North American Actuarial Journal 18 (1):150–67. doi:10.1080/10920277.2013.872553
   Google Scholar