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ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 10 (October 2019), Pages: 53-61

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 Original Research Paper

 Title: A new approach to forecast Malaysian mortality rates

 Author(s): Sajjad Majeed Bin Iqbal Hamid 1, *, Eric Dei Ofosu-Hene 2, Raja Rajeswari Ponnusamy 1

 Affiliation(s):

 1School of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia
 2Department of Accounting and Finance, Faculty of Business and Law, De Montfort University, Leicester, UK

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-0877-7477

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.10.010

 Abstract:

In this paper a new approach to project the time varying index kt for the Lee-Carter (LC) model by using a machine learning technique known as Neural Network (NN) is proposed for forecasting Malaysian male and female mortality rates. To evaluate the forecasting performance of the proposed model, the conventional LC model which uses ARIMA to forecast kt is used as a benchmark. However unlike previous studies were done in Malaysian, we employed 9 different ARIMA models and evaluated the AIC and BIC to obtain the best fit model to forecast kt. The forecasting performance of the two methodologies were then compared using 3 performance indicators Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). In this study, findings showed that the proposed NN model outperformed the conventional ARIMA model for forecasting both Malaysian male and female mortality rates. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Forecasting, Lee-Carter, Time varying index, ARIMA, Neural network

 Article History: Received 11 April 2019, Received in revised form 3 August 2019, Accepted 3 August 2019

 Acknowledgement:

No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Hamid SMBI, Ofosu-Hene ED, and Ponnusamy RR (2019). A new approach to forecast Malaysian mortality rates. International Journal of Advanced and Applied Sciences, 6(10): 53-61

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

 Tables

 Table 1 Table 2

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 References (45) 

  1. Aberathna W, Alles L, Wickremasinghe WN, and Hewapathirana I (2014). Modeling and forecasting mortality in Sri Lanka. Sri Lankan Journal of Applied Statistics, 15(3): 141-170. https://doi.org/10.4038/sljastats.v15i3.7794   [Google Scholar]
  2. Antonio K, Bardoutsos A, and Ouburg W (2015). Bayesian Poisson log-bilinear models for mortality projections with multiple populations. European Actuarial Journal, 5(2): 245-281. https://doi.org/10.1007/s13385-015-0115-6   [Google Scholar]
  3. Bell WR (1997). Comparing and assessing time series methods for forecasting age-specific fertility and mortality rates. Journal of Official Statistics, 13(3): 279-303.   [Google Scholar]
  4. Booth H, Maindonald J, and Smith L (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3): 325-336. https://doi.org/10.1080/00324720215935   [Google Scholar] PMid:12553330
  5. Box GE, Jenkins GM, Reinsel GC, and Ljung GM (2015). Time series analysis: Forecasting and control. John Wiley and Sons, Hoboken, USA.   [Google Scholar]
  6. Brouhns N, Denuit M, and Vermunt JK (2002). A Poisson log-bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31(3): 373-393. https://doi.org/10.1016/S0167-6687(02)00185-3   [Google Scholar]
  7. Burnham KP and Anderson DR (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2): 261-304. https://doi.org/10.1007/b97636   [Google Scholar]
  8. Carter LR (1996). Forecasting US mortality: A comparison of Box-Jenkins ARIMA and structural time series models. The Sociological Quarterly, 37(1): 127-144. https://doi.org/10.1111/j.1533-8525.1996.tb02334.x   [Google Scholar]
  9. Cerda-Hernández J and Sikov A (2018). Lee-Carter method for forecasting mortality for Peruvian population. arXiv:1811.09622. Available online at: https://bit.ly/2ORykzV   [Google Scholar]
  10. Chavhan R and Shinde R (2016). Modeling and forecasting mortality using the Lee-Carter model for Indian population based on decade-wise data. Sri Lankan Journal of Applied Statistics, 17(1): 51-68. https://doi.org/10.4038/sljastats.v17i1.7845   [Google Scholar]
  11. Creal D, Koopman SJ, and Lucas A (2008). A general framework for observation driven time-varying parameter models. Tinbergen Institute Discussion Paper No. 08-108/4, Amsterdam, Netherlands. https://doi.org/10.2139/ssrn.1297183   [Google Scholar]
  12. Creal D, Koopman SJ, and Lucas A (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5): 777-795. https://doi.org/10.1002/jae.1279   [Google Scholar]
  13. De Jong P and Tickle L (2006). Extending Lee–Carter mortality forecasting. Mathematical Population Studies, 13(1): 1-18. https://doi.org/10.1080/08898480500452109   [Google Scholar]
  14. Deaton AS and Paxson C (2004). Mortality, income, and income inequality over time in Britain and the United States. In: Wise DA (Ed.), Perspectives on the economics of aging: 247-286. University of Chicago Press, Chicago, USA. https://doi.org/10.7208/chicago/9780226903286.003.0007   [Google Scholar]
  15. Girosi F and King G (2007). Understanding the Lee-Carter mortality forecasting method. Working Paper, Harvard University, Cambridge, USA.   [Google Scholar]
  16. Harvey AC (1990). Forecasting, structural time series models and the Kalman filter. Cambridge University Press, Cambridge, USA. https://doi.org/10.1017/CBO9781107049994   [Google Scholar]
  17. Hyndman RJ and Ullah MS (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics and Data Analysis, 51(10): 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028   [Google Scholar]
  18. Jiang L (1995). Changing kinship structure and its implications for old-age support in urban and rural China. Population Studies, 49(1): 127-145. https://doi.org/10.1080/0032472031000148286   [Google Scholar]
  19. Koissi MC and Shapiro AF (2006). Fuzzy formulation of the Lee–Carter model for mortality forecasting. Insurance: Mathematics and Economics, 39(3): 287-309. https://doi.org/10.1016/j.insmatheco.2005.11.011   [Google Scholar]
  20. Koissi MC, Shapiro AF, and Högnäs G (2006). Evaluating and extending the Lee–Carter model for mortality forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38(1): 1-20. https://doi.org/10.1016/j.insmatheco.2005.06.008   [Google Scholar]
  21. Kourentzes N (2017). NNFOR: Time series forecasting with neural networks. GPL-3, Package. Available online at: https://bit.ly/2Z4agOk   [GoogleScholar]
  22. Kourentzes N and Crone S (2010a). Feature selection for time series prediction: A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10-12): 1923-1936. https://doi.org/10.1016/j.neucom.2010.01.017   [Google Scholar]
  23. Kourentzes N and Crone SF (2010b). Frequency independent automatic input variable selection for neural networks for forecasting. In The 2010 International Joint Conference on Neural Networks, IEEE, Barcelona, Spain: 1-8. https://doi.org/10.1109/IJCNN.2010.5596637   [Google Scholar]
  24. Kourentzes N, Barrow DK, and Crone SF (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9): 4235-4244. https://doi.org/10.1016/j.eswa.2013.12.011   [Google Scholar]
  25. Lee R and Miller T (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography, 38(4): 537-549. https://doi.org/10.1353/dem.2001.0036   [Google Scholar]PMid:11723950
  26. Lee RD and Carter LR (1992). Modeling and forecasting US mortality. Journal of the American Statistical Association, 87(419): 659-671. https://doi.org/10.1080/01621459.1992.10475265   [Google Scholar]
  27. Lee RD and Rofman R (1994). Modeling and projecting mortality in Chile. Notas de Poblacion, 22(59): 183-213.   [Google Scholar]
  28. Li N and Lee R (2005). Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography, 42(3): 575-594. https://doi.org/10.1353/dem.2005.0021   [Google Scholar] PMid:16235614 PMCid:PMC1356525
  29. Neves C, Fernandes C, and Hoeltgebaum H (2017). Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models. Insurance: Mathematics and Economics, 75: 48-57. https://doi.org/10.1016/j.insmatheco.2017.04.004   [Google Scholar]
  30. Ngataman N, Ibrahim RI, and Yusuf MM (2016). Forecasting the mortality rates of Malaysian population using Lee-Carter method. In the AIP Conference Proceedings, AIP Publishing, Melville, USA, 1750(1): 020009. https://doi.org/10.1063/1.4954522   [Google Scholar]
  31. Raftery AE, Chunn JL, Gerland P, and Ševčíková H (2013). Bayesian probabilistic projections of life expectancy for all countries. Demography, 50(3): 777-801. https://doi.org/10.1007/s13524-012-0193-x   [Google Scholar] PMid:23494599 PMCid:PMC3904289
  32. Ramsay JO and Silverman BW (2005). Principal components analysis for functional data. In: Ramsay JO and Silverman BW (Eds.), Functional data analysis: 147-172. Springer, New York, USA. https://doi.org/10.1007/b98888   [Google Scholar]
  33. Remund A, Camarda CG, and Riffe T (2018). Analyzing the young adult mortality hump in R with MortHump. MPIDR Technical Report No. TR-2018-2018-003, Max Planck Institute for Demographic Research, Rostock, Germany.   [Google Scholar]
  34. Safitri L, Mardiyati S, and Rahim H (2018). Forecasting the mortality rates of Indonesian population by using neural network. In the International Conference on Mathematics: Pure, Applied and Computation, Journal of Physics: IOP Publishing, 974(1): 012030. https://doi.org/10.1088/1742-6596/974/1/012030   [Google Scholar]
  35. Tuljapurkar S, Li N, and Boe C (2000). A universal pattern of mortality decline in the G7 countries. Nature, 405(6788): 789-792. https://doi.org/10.1038/35015561   [Google Scholar] PMid:10866199
  36. Wilmoth JR (1993). Computational methods for fitting and extrapolating the Lee-Carter model of mortality change. Technical Report, Department of Demography, University of California, Berkeley, USA.   [Google Scholar]
  37. Wilmoth JR (1996). Mortality projections for Japan: A comparison of four methods. In: Caselli G and Lopez A (Eds.), Health and mortality among elderly population: 266-287. Oxford University Press, Oxford, UK.   [Google Scholar]
  38. Wilmoth JR, Andreev K, Jdanov D, Glei DA, Boe C, Bubenheim M, and Vachon P (2007). Methods protocol for the human mortality database. University of California, Berkeley, USA and Max Planck Institute for Demographic Research, Rostock, Germany.   [Google Scholar]
  39. Wiśniowski A, Smith PW, Bijak J, Raymer J, and Forster JJ (2015). Bayesian population forecasting: Extending the Lee-Carter method. Demography, 52(3): 1035-1059. https://doi.org/10.1007/s13524-015-0389-y   [Google Scholar] PMid:25962866
  40. Wong JS, Forster JJ, and Smith PW (2018). Bayesian mortality forecasting with overdispersion. Insurance: Mathematics and Economics, 83: 206-221. https://doi.org/10.1016/j.insmatheco.2017.09.023   [Google Scholar]
  41. Yadav A, Yadav S, and Kesarwani R (2012). Decelerating mortality rates in older ages and its prospects through Lee-Carter approach. PloS One, 7(12): e50941. https://doi.org/10.1371/journal.pone.0050941   [Google Scholar] PMid:23236414 PMCid:PMC3516525
  42. Yan W (2012). Toward automatic time-series forecasting using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 23(7): 1028-1039. https://doi.org/10.1109/TNNLS.2012.2198074   [Google Scholar] PMid:24807130
  43. Yasungnoen N and Sattayatham P (2016). Forecasting Thai mortality by using the Lee-Carter model. Asia-Pacific Journal of Risk and Insurance, 10(1): 91-105.   [Google Scholar]
  44. Yue JC, Yang SS, and Huang HC (2008). A study of the Lee-Carter model with age-shifts. In the Living to 100 and Beyond Symposium, Orlando, USA: 1-20.   [Google Scholar]
  45. Zhang G, Patuwo BE, and Hu MY (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1): 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7   [Google Scholar]