Forecasting life expectancy in an international context
AbstractOver the past two centuries, the life expectancy has more than doubled in many countries, for both males and females. The levels of the countries with the highest life expectancies have risen almost linearly. We exploit this regularity by using the classic univariate ARIMA model to forecast future levels of best-practice life expectancy. We then compare two alternative stochastic models for forecasting the gap between the best-practice level and life expectancy in a particular population. One of our approaches is based on the concept of discrete geometric Brownian motion; our other approach relies on a discrete model of geometric mean-reverting processes. A key advantage of our strategy is that the life expectancies forecast for different countries are positively correlated because of their tie to the forecast best-practice line. We provide illustrations based on Italian and US data.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Best-practice levels; Geometric Brownian motion; Geometric mean-reverting process; ARIMA models; Monte Carlo simulation;
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- Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
- Jacques Vallin & France Meslé, 2009. "The Segmented Trend Line of Highest Life Expectancies," Population and Development Review, The Population Council, Inc., vol. 35(1), pages 159-187.
- Kirill F. Andreev & James W. Vaupel, 2006. "Forecasts of cohort mortality after age 50," MPIDR Working Papers WP-2006-012, Max Planck Institute for Demographic Research, Rostock, Germany.
- Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
- Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer, vol. 42(3), pages 575-594, August.
- Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer, vol. 50(3), pages 777-801, June.
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