Page 116 - Contributed Paper Session (CPS) - Volume 8

CPS2210 Justyna Majewska et al.
Lee (2005) noted that a model with the highest value of ER signifies the best
fit to the data. The lowest MAE and MAPE indicate for a better fit to historical
data as well.
Table 1. In-sample goodness-of-fit measures
LC for each country separately Two-population models
Explanation ratio 0.8538 0.9251
Mean absolute error 0.0061 0.0049
Mean absolute percentage 1.361 1.054
error

4. Discussion and Conclusion
We compared the two-population mortality model by Li and Lee (2005)
and Lee-Carter model for ech country independently. We notice that the
parameters for two-population model for each pair of populations look
similar. There are some differences between the age-specific component in
model Poland-Lithuania and rest of models. As expected, all of the common
parameters behave similarly, which is an indication that the models capture
the common trend between Poland and other countries.
The historical period for in-sample fitting is ranged from the year 1950
until the year 2015. The results in Table 1 suggest that the augmented
common factor model shows the best in-sample error performances as
compared to the independent model.

References
1. 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.
2. Brouhns, N., Denuit, M., & Vermunt, J. (2002). A Poisson log-bilinear
regression approach to the construction of projected lifetables.
Insurance: Mathematics and Economics, 31(3), 373–393.

3. 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 and Insurance, 73(4), 687–718.

4. 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.
5. 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.
6. Currie, I. D., Durban, M., & Eilers, P. H. C. (2004). Smoothing and
forecasting mortality rates. Statistical Modelling, 4(4), 279–298.

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