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Volume 14
Issue 1
Online publication date 2018-01-16
Title Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model
Author Sallahuddin Hassan, Zalila Othman
Abstract
This study employs the use of Box-Jenkins’ ARIMA (1,1,0) model for the estimation and forecasts based on the annual data of EPF balances, which serve as a proxy to EPF sustainability, together with the yearly data of possible determinants namely investment earnings, nominal income, elderly population, life expectancy and mortality rate in Malaysia for the 1960 – 2010 and 2010 - 2014 periods, respectively. Amid a negative sentiment and conceivably bleak outlook on the long term EPF inadequacy to provide adequate incomes to elderly persons, the prognosis of this study instead reveals otherwise and is found to be in support for the long term prospect and sustainability of the EPF. With necessary improvements are underway to strengthen the performance of the administered EPF system, it is likely to believe that the EPF organization is committed to promoting its product as a more inclusive and equitable scheme in Malaysia.    
Citation
References

Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting.  Germany: LAP Lambert Academic Publishing.   

Ahmad, S. Y. & Sabri, M. F. (2014). Understanding financial security from consumer’s perspective: A review of literature. International Journal of Humanities and Social Science, 4(12), 110-117. 

Asher, M. G., & Bali, A. S. (2015). Public pension programme in Southeast Asia: An assessment. Asian Economic Policy Review, 10, 225-245.

Bank Negara Malaysia (2000). Annual report 2000. Retrieved from http://www.bnm.gov.my/.  

Bowerman, B. L., Richard, T. C. and Koehler, A. B. (2005). Forecasting, time series and regression, fourth edition, Belmont, CA: Thomson Brooks/Cole. 

Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis, forecasting and control. San Francisco: Holden Day. 

Box, G. E. P. & Jenkins, G. M. (1976). Time series analysis, forecasting and control, revised edition. San Francisco: Holden Day.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.

EPF (2010). Annual report 2010. Retrieved October 15, 2017, from http://www.kwsp.gov.my/. 

Jaafar, R., & Daly, K. J. (2016). Reviewing the financial soundness of the Malaysia’s Employees Provident Fund. International Journal of Humanities and Management Sciences, 4(1), 14-18.

Karin, K. (2011). Forecasting electricity demand in Thailand with an artificial neural network approach. Energies, 4, 1246-1257.

Li, Q., Guo, N-N., Han, Z-Y., Zhang, Y-B., Qi, S-X., Xu, Y-G., Wei, Y-M., Han, X., & Liu, Y-Y. (2012). Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. The American Journal Society of Tropical Medicine and Hygiene, 87(2), 364-370.   

Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16, 147-163. 
Meyler, A., Kenny, G., & Terry, Q. (1998). Forecasting Irish inflation using ARIMA models. MPRA Paper No. 11359, Munich Personal RePEc Archive. Retrieved from http://mpra.ub.uni-muenchen.de/11359/. 

Mohd, S. (2013). Provident fund in Malaysia: Sustainability of retirement income provision. Paper presented at the third Asia-Pacific Business Research Conference, Kuala Lumpur, Malaysia. 

Moosazadeh, M., Nasehi, M., Bahrampour, A., Khanjani, N., Sharafi, S. & Ahmadi, S. (2014). Forecasting tuberculosis incidence in Iran using Box-Jenkins models. Iranian Red Crescent Medical Journal, 16(5), 1-6.  

Nanda, S. (1988). Forecasting: Does the Box-Jenkins method work better than regression? The Journal for Decision Maker, 13(1), 53-62. 

Narayanan, S. (2002). Old age support for private sector employees in Malaysia: Can the employees providen fund do better? Hitotsubashi Journal of Economics, 43, 119-134.

Phillips, P. C. B., & Perron, P. (1988). Testing unit root in time series regression. Biometrica, 5, 335-346.

Pindyck, R. S., & Rubinfeld, D. L. (1991). Econometric models and economic forecasts, third edition, New York: McGraw Hill.  

Promprou, S., Jaroensutasinee, M., & Jaroensutasinee, K. (2006). Forecasting dengue haemorrhagic fever cases in Southern Thailand using ARIMA models. Dengue Bulletin, 30, 99-106.   

Salam, M. A., Salam, S., & Feridun, M. (2007). Modeling and forecasting Pakistan’s inflation by using time series ARIMA models. Economic Analysis Working Paper 6. Retrieved October 15, 2017, from http://www.unagaliciamoderna.com/

Samad, S. A., & Mansor, N. (2013). Population ageing and social protection in Malaysia. Malaysian Journal of Economic Studies, 50(2), 139-156.

Tan, C. F. (2007). The linkage between employees provident fund and Malaysia economic growth. Dissertation submitted for Bachelor of Science Degree with Honours, School of Science and Technology, Universiti Malaysia Sabah. 

Zhang, X., Zhang, T., Young, A. A., & Li, X. (2014). Applications and comparisons of four time series models in epidemiological surveillance data. PLOS ONE, 9(2), 1-16.

Keywords ARIMA model, EPF, forecasting, long-term sustainability
DOI http://dx.doi.org/10.15208/beh.2018.4
Pages 43-53
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