In the latter part of the 20th century, continued improvements in living standards, health behaviors, and medical care reduced mortality and produced amazing advances in life expectancy. These trends, followed by all industrial nations, decidedly affect the financial position of an insurance company, interested in the construction of updated life tables. The approach to this problem is faced in this paper by using the Lee-Carter methodology. In particular, in the present work, we are interested in modeling and forecasting mortality and life expectancy on a period basis through the use of a stochastic forecasting method which uses time-series models to make long-term forecasts.
 Brouhns, N., Denuit, M. and Vermunt, J.K. (2002) A Poisson Log-Bilinear Regression Approach to the Construction of Projected Life Tables. Insurance: Mathematics and Economics, 31, 373-393. http://dx.doi.org/10.1016/S0167-6687(02)00185-3
 Renshaw, A. and Haberman, S. (2003) Lee-Carter Mortality Forecasting: A Parallel Generalised Linear Modelling Approach for England and Wales Mortality Projections. Applied Statistics, 52, 119-137. http://dx.doi.org/10.1111/1467-9876.00393
 Koissi, M.C., Shapiro, A. and H?gn?s, G. (2005) Fitting and Forecasting Mortality Rates for Nordic Countries Using the Lee-Carter Method. Department of Mathematics, Abo Academy University, Finland.
 Giordano, G., Russolillo, M. and Haberman, S. (2008) Comparing Mortality Trends via Lee Carter Method in the Framework of Multidimensional Data Analysis. Mathematical and Statistical Methods in Insurance and Finance. Springer Verlag, Berlin, 131-138.
 Eckart, C. and Young, G. (1936) The Approximation of One Matrix by Another of Lower Rank. Psychometrika, 1, 211-218. http://dx.doi.org/10.1007/BF02288367
 (2009) Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). www.mortality.org or www.humanmortality.de