Alho, J., & Spencer, B. D. (2005). *Statistical demography and forecasting.* Springer Science + Business Media, Inc

Armstrong, J. S. (2001). *Principles of forecasting: a handbook for researchers and practitioners.* Springer Science + Business Media

Basellini, U., & Camarda, C. G. (2016). Modeling and forecasting age at death distributions. Paper presented at the *PAA Annual Meeting 2016*, Washington, D.C., USA

Billari, F. C., Graziani, R., & Melilli, E. (2012). Stochastic population forecasts based on conditional expert opinions. *Journal of the Royal Statistical Society, Series A, 175*(2), 491–511.

Article
Google Scholar

Billari, F. C., Graziani, R., & Melilli, E. (2014). Stochastic population forecasting based on combinations of expert evaluations within the Bayesian paradigm. *Demography, 51*(5), 1933–1954.

Article
Google Scholar

Billari, F., Corsetti, G., Graziani, R., Marsili, M., Melilli, E. (2014b). Towards stochastic forecasts of the Italian population: an experiment with conditional expert elicitations. *Proceedings of the Sixth Eurostat/UNECE Work Session on Demographic Projections*, 326–338.

Bohk, C., & Rau, R. (2014). Mortality forecasts with a flexible age pattern of change for several European countries. *Proceedings of the Sixth Eurostat/UNECE Work Session on Demographic Projections*, 360–371.

Bohk, C., & Rau, R. (2016). Changing mortality patterns and their predictability: the case of the United States. In R. Schoen (Ed.), *Dynamic Demographic Analysis. The Springer Series on Demographic Methods and Population Analysis*, Volume 39, (pp. 69–89). Springer International Publishing Switzerland 2016.

Booth, H. (2006). Demographic forecasting: 1980 to 2005 in review. *International Journal of Forecasting, 22*(3), 547–581.

Article
Google Scholar

Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. *Annals of Actuarial Science, 3*(1–2), 3–43.

Article
Google Scholar

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*(9), 289–310.

Article
Google Scholar

Butt, Z., & Haberman, S. (2010a). A comparative study of parametric mortality projection models. *Actuarial Research Paper* No. 196. London, UK: Faculty of Actuarial Science and Insurance, City University London.

Butt, Z., & Haberman, S. (2010b). ilc: a collection of R functions for fitting a class of Lee-Carter mortality models using iterative fitting algorithms. *Actuarial Research Paper* No. 190, Cass Business School, Faculty of Actuarial Science and Insurance, London.

Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., & Khalaf-Allah, M. (2011). Bayesian stochastic mortality modelling for two populations. *astin bulletin, 41*(1), 29–59.

Google Scholar

Camarda, C. G. (2012). MortalitySmooth: an R package for smoothing poisson counts with P-splines. *Journal of Statistical Software, 50*(1), 1–24.

Article
Google Scholar

Carlin, B. P., & Louis, T. A. (2008). *Bayesian methods for data analysis*. Chapman & Hall/CRC

Christensen, K., Doblhammer, G., Rau, R., & Vaupel, J. W. (2009). Ageing populations: the challenges ahead. *The Lancet, 374*(9696), 1196–1208.

Article
Google Scholar

Christensen, K., Davidsen, M., Juel, K., Mortensen, L., Rau, R., & Vaupel, J. W. (2010). The divergent life-expectancy trends in Denmark and Sweden—and some potential explanations. In E. M. Crimmins, S. H. Preston, & B. Cohen (Eds.), *International differences in mortality at older ages: dimensions and sources* (pp. 385–407). Washington DC: The National Academies Press.

Google Scholar

Coelho, E., & Nunes, L. C. (2011). Forecasting mortality in the event of a structural change. *Journal of the Royal Statistical Society, Series A, 174*(3), 713–736.

Article
Google Scholar

Currie, I. D., Durban, M., & Eilers, P. H. C. (2004). Smoothing and forecasting mortality rates. *Statistical Modelling, 4*(4), 279–298.

Article
Google Scholar

Czado, C., Delwarde, A., & Denuit, M. (2005). Bayesian Poisson log-bilinear mortality projections. *Insurance: Mathematics and Economics, 36*(3), 260–284.

Google Scholar

Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. *Statistical Science, 11*(2), 89–121.

Article
Google Scholar

European Commission (2011). The 2012 ageing report: underlying assumptions and projection methodologies. European Economy 4. http://ec.europa.eu/economy_finance/publications/european_economy/2011/pdf/ee-2011-4_en.pdf. Accessed 29 June 2016

European Commission (2014). The 2015 ageing report: underlying assumptions and projection methodologies. European Economy 8. http://ec.europa.eu/economy_finance/publications/european_economy/2014/pdf/ee8_en.pdf. Accessed 29 June 2016

French, D., & O’Hare, C. (2014). Forecasting death rates using exogenous determinants. *Journal of Forecasting, 33*, 640–650.

Article
Google Scholar

Frenk, J., Bobadilla, J. L., Stern, C., Frejka, T., & Lozano, R. (1991). Elements for a theory of the health transition. *Health Transition Review, 1*(1), 21–38.

Google Scholar

Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. *Bayesian Analysis, 1*(3), 515–533.

Google Scholar

Gelman, A., Carlin, J. B., Stern, H. S. (2003). *Bayesian data analysis*. CRC Press Inc.

Gelman, A., & Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., Rubin, D. B. (2014). *Bayesian data analysis. Third edition*. CRC Press Inc.

Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. *IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 6*(6), 721–741.

Article
Google Scholar

Girosi, F., & King, G. (2008). *Demographic forecasting*. Princeton University Press

Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. *Journal of the Royal Statistical Society, Series B, 69*(2), 243–268.

Article
Google Scholar

Haberman, S., & Renshaw, A. E. (2012). Parametric mortality improvement rate modelling and projecting. *Insurance: Mathematics and Econometrics, 50*(3), 309–333.

Google Scholar

Horiuchi, S., & Wilmoth, J. (1995). Aging of mortality decline. Presented at the *Annual Meeting of the Population Association of America*, San Francisco, California, April 6–8, 1995

Human Mortality Database (2013). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available at www.mortality.org.

Hyndman, R. J., & Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: a functional data approach. *Computational Statistics and Data Analysis, 51*, 4942–4956.

Article
Google Scholar

Hyndman, R. J., Booth, H., & Yasmeen, F. (2013). Coherent mortality forecasting: the product-ratio method with functional time series models. *Demography, 50*(1), 261–283.

Article
Google Scholar

Hyndman, R. J., with contributions from Booth, H., Tickle, L., Maindonald, J. (2015). Package demography: forecasting mortality, fertility, migration and population data. R package, Version 1.18

Jackman, S. (2009). *Bayesian analysis for the Social Sciences*. John Wiley & Sons, Ltd

Jacobsen, R., Keiding, N., & Lynge, E. (2002). Long term mortality trends behind low life expectancy of Danish women. *Journal of Epidemiology and Community Health, 56*(3), 205–208.

Article
Google Scholar

Janssen, F., & de Beer, J. (2016). Projecting future mortality in the Netherlands taking into account mortality delay and smoking. *Joint Eurostat/UNECE Work Session on Demographic Projections*, Geneva, 18–20 April 2016

Janssen, F., & Kunst, A. (2007). The choice among past trends as a basis for the prediction of future trends in old-age mortality. *Population Studies, 61*(3), 315–326.

Article
Google Scholar

Janssen, F., van Wissen, L. J. G., & Kunst, A. E. (2013). Including the smoking epidemic in internationally coherent mortality projections. *Demography, 50*(4), 1341–1362.

Article
Google Scholar

Janssen, F., Rousson, V., & Paccaud, F. (2015). The role of smoking in changes in the survival curve: an empirical study in 10 European countries. *Annals of Epidemiology, 25*, 243–249.

Article
Google Scholar

Kannisto, V., Lauritsen, J., Thatcher, A. R., & Vaupel, J. W. (1994). Reductions in mortality at advanced ages: several decades of evidence from 27 countries. *Population and Development Review, 20*(4), 793–810.

Article
Google Scholar

Keilman, N. (2008). European demographic forecasts have not become more accurate over the past 25 years. *Population and Development Review, 34*(1), 137–153.

Article
Google Scholar

Keyfitz, N. (1977). *Applied mathematical demography*. New York: John Wiley & Sons.

Google Scholar

King, R. (2012). *Bayesian analysis for population ecology*. Chapman and Hall/CRC

King, G., & Soneji, S. (2011). The future of death in America. *Demographic Research, 25*(1), 1–38.

Google Scholar

Kogure, A., & Kurachi, Y. (2010). A Bayesian approach to pricing longevity risk based on risk-neutral predictive distributions. *Insurance: Mathematics and Economics, 46*(1), 162–172.

Google Scholar

Koller, D., & Friedman, N. (2009). *Probabilistic graphical models: principles and techniques.* Adaptive Computation and Machine Learning series. The MIT Press

Kruschke, J. K. (2011). *Doing Bayesian data analysis. a tutorial with R and BUGS.* Academic Press, Elsevier

Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. *Journal of the American Statistical Association, 87*(419), 659–671.

Google Scholar

Levins, R. (1966). The strategy of model building in population biology. *American Scientist, 54*(4), 421–431.

Google Scholar

Li, N., & Lee, R. (2005). Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method. *Demography, 42*(3), 575–594.

Article
Google Scholar

Li, N., Lee, R., & Gerland, P. (2013). Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. *Demography, 50*(6), 2037–2051.

Article
Google Scholar

Lindahl-Jacobsen, R., Rau, R., Jeunea, B., Canudas-Romo, V., Lenart, A., Christensen, K., & Vaupel, J. W. (2016). Rise, stagnation, and rise of Danish women’s life expectancy. *Proceedings of the National Academy of Sciences of the United States of America, 113*(15), 4015–4020.

Article
Google Scholar

Lopez, A. D., Collishaw, N. E., & Piha, T. (1994). A descriptive model of the cigarette epidemic in developed countries. *Tobacco Control, 3*, 242–247.

Article
Google Scholar

Mitchell, D., Brockett, P., Mendoza-Arriage, R., & Muthuraman, K. (2013). Modeling and forecasting mortality rates. *Insurance: mathematics and economics, 52*(2), 275–285.

Google Scholar

OECD (2011). Pensions at a glance 2011: retirement-income systems in OECD and G20 countries. OECD Publishing. http://dx.doi.org/10.1787/pension_glance-2011-en. Accessed 29 June 2016

Oeppen, J. (2008). Coherent forecasting of multiple-decrement life tables: a test using Japanese cause of death data. Paper presented at the *European Population Conference* 2008, Barcelona, Spain

Oeppen, J., & Vaupel, J. W. (2002). Broken limits to life expectancy. *Science, 296*(5570), 1029–1031.

Article
Google Scholar

Omran, A. (1971). The epidemiological transition. A theory of the epidemiology of population change. *The Milbank Memorial Fund Quarterly, 49*(4), 509–538.

Article
Google Scholar

Orzack, S. H. (2012). The philosophy of modelling or does the philosophy of biology have any use? *Philosophical Transactions of the Royal Society B, 367*(1586), 170–180.

Article
Google Scholar

Pedroza, C. (2006). A Bayesian forecasting model: predicting U.S. male mortality. *Biostatistics, 7*(4), 530–550.

Article
Google Scholar

Plummer, M. (2012). JAGS Version 3.3.0 user manual. http://people.math.aau.dk/~kkb/Undervisning/Bayes14/sorenh/docs/jags_user_manual.pdf. Accessed 29 June 2016.

Plummer, M. (2015). Package rjags: Bayesian graphical models using MCMC. R package version 3–15

Preston, S. H., & Stokes, A. (2012). Sources of population aging in more and less developed countries. *Population and Development Review, 38*(2), 221–236.

Article
Google Scholar

R Core Team (2012). *R: a language and environment for statistical computing*. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.

Google Scholar

Raftery, A. E., & Lewis, S. M. (1992). Comment: One long run with diagnostics: implementation strategies for Markov Chain Monte Carlo. *Statistical Science, 7*(4), 493–497.

Article
Google Scholar

Raftery, A. E., Chunn, J. L., Gerland, P., & Ševčíková, H. (2013). Bayesian probabilistic projections of life expectancy for all countries. *Demography, 50*(3), 777–801.

Article
Google Scholar

Rau, R., Jasilionis, D., Soroko, E. L., & Vaupel, J. W. (2008). Continued reductions in mortality at advanced ages. *Population and Development Review, 34*(4), 747–768.

Article
Google Scholar

Renshaw, A. E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. *Insurance: Mathematics and Economics, 33*(2), 255–272.

Google Scholar

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.

Google Scholar

Russolillo, M., Giordano, G., & Haberman, S. (2011). Extending the Lee-Carter model: a three-way decomposition. *Scandinavian Actuarial Journal, 2011*(2), 96–117.

Article
Google Scholar

Schmertmann, C., Zagheni, E., Goldstein, J. R., & Myrskylä, M. (2014). Bayesian forecasting of cohort fertility. *Journal of the American Statistical Association, 109*(506), 500–513.

Article
Google Scholar

Ševčíková, H., & Raftery, A. E. (2015). Package bayesLife: Bayesian projection of life expectancy. R package version 2.2-0. Original WinBugs code written by Jennifer Chunn.

Ševčíková, H., Li, N., Kantorová, V., Gerland, P., Raftery, A. E. (2016). Age-specific mortality and fertility rates for probabilistic population projections. In R. Schoen (Ed.), *Dynamic Demographic Analysis. The Springer Series on Demographic Methods and Population Analysis*, Volume 39, (pp. 69–89). Springer International Publishing Switzerland 2016.

Shang, H. L. (2012). Point and interval forecasts of age-specific life expectancies: a model averaging approach. *Demographic Research, 27*(21), 593–644.

Article
Google Scholar

Shang, H. L., Booth, H., & Hyndman, R. (2011). Point and interval forecasts of mortality rates and life expectancy: a comparison of ten principal component methods. *Demographic Research, 25*(5), 173–214.

Article
Google Scholar

Soneji, S., & King, G. (2012). Statistical security for social security. *Demography, 49*(3), 1037–1060.

Article
Google Scholar

Stoeldraijer, L., van Duin, C., van Wissen, L., & Janssen, F. (2013). Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: the case of the Netherlands. *Demographic Research, 29*(13), 323–354.

Article
Google Scholar

Stoeldraijer, L., Bonneux, L., van Duin, C., van Wissen, L., & Janssen, F. (2014). The future of smoking-attributable mortality: the case of England & Wales, Denmark and the Netherlands. *Addiction, 110*, 336–345.

Article
Google Scholar

Su, Y.-S., & Yajima, M. (2015). Package R2jags: using R to run jags. R package version 0.05-6. http://cran.r-project.org/web/packages/R2jags/R2jags.pdf Accessed 29 June 2016

Thun, M., Peto, R., Boreham, J., & Lopez, A. D. (2012). Stages of the cigarette epidemic on entering its second century. *Tobacco Control, 21*, 96–101.

Article
Google Scholar

Torri, T., & Vaupel, J. (2012). Forecasting life expectancy in an international context. *International Journal of Forecasting, 28*, 519–531.

Article
Google Scholar

Tuljapurkar, S., Li, N., & Boe, C. (2000). A universal pattern of mortality decline in the G7 countries. *Nature, 405*, 789–792.

Article
Google Scholar

United Nations (1982). Model life tables for developing countries. Sales no. e.81.xiii.7, United Nations publication. http://www.un.org/esa/population/publications/Model_Life_Tables/Model_Life_Tables.htm. Accessed 29 June 2016.

United Nations. (2013). *World population prospects: the 2012 revision*. New York: United Nations.

Google Scholar

Vallin, J., & Meslé, F. (2004). Convergences and divergences in mortality. A new approach to health transition. *Demographic Research, Special collection, 2*(2), 11–44.

Article
Google Scholar

Vallin, J., & Meslé, F. (2009). The segmented trend line of highest life expectancies. *Population and Development Review, 35*(1), 159–187.

Article
Google Scholar

van Berkum, F., Antonio, K., Vellekoop, M. (2013). Structural changes in mortality rates with an application to Dutch and Belgian data. *AFI 1379 Working Paper*, KU Leuven, Leuven

Vaupel, J. W. (1997). The remarkable improvements in survival at older ages. *Philosophical Transactions of the Royal Society B, 352*(1363), 1799–1804.

Article
Google Scholar

Vaupel, J. W. (2010). Biodemography of human ageing. *Nature, 464*, 536–542.

Article
Google Scholar

Wang, H., & Preston, S. H. (2009). Forecasting United States mortality using cohort smoking histories. *Proceedings of the National Academy of Sciences of the United States of America, 106*(2), 393–398.

Article
Google Scholar

White, K. M. (2002). Longevity advances in high-income countries, 1955–96. *Population and Development Review, 28*(1), 59–76.

Article
Google Scholar

Wiśniowski, A., Smith, P. W. F., Bijak, J., Raymer, J., & Forster, J. J. (2015). Bayesian population forecasting: extending the Lee-Carter method. *Demography, 52*(3), 1035–1059.

Article
Google Scholar