IDEAS home Printed from https://ideas.repec.org/a/pdc/jrnbeh/v14y2018i1p43-53.html
   My bibliography  Save this article

Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model

Author

Listed:
  • 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.

Suggested Citation

  • Sallahuddin Hassan & Zalila Othman, 2018. "Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 43-53, January.
  • Handle: RePEc:pdc:jrnbeh:v:14:y:2018:i:1:p:43-53
    DOI: 10.15208/beh.2018.4
    as

    Download full text from publisher

    File URL: https://academicpublishingplatforms.com/downloads/pdfs/beh/volume30/201803301234_04_BEH_2018_Vol14_Issue1_Hassan_and_Othman_Forecasting_long-term_sustainability_ARIMA_pp.43-53.pdf
    Download Restriction: no

    File URL: https://academicpublishingplatforms.com/volume.php?journal=BEH&id=3&number=30
    Download Restriction: no

    File URL: https://libkey.io/10.15208/beh.2018.4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    2. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    3. Mukul Asher & Azad S. Bali, 2015. "Public Pension Programs in Southeast Asia: An Assessment," Asian Economic Policy Review, Japan Center for Economic Research, vol. 10(2), pages 225-245, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jaafar, Roslan & Daly, Kevin James & Mishra, Anil V., 2019. "Challenges facing Malaysia pension scheme in an era of ageing population," Finance Research Letters, Elsevier, vol. 30(C), pages 334-340.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    2. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," Research Technical Papers 4/RT/98, Central Bank of Ireland.
    3. Syarifah Inayati & Nur Iriawan & Irhamah, 2024. "A Markov Switching Autoregressive Model with Time-Varying Parameters," Forecasting, MDPI, vol. 6(3), pages 1-23, July.
    4. Jerelyn Co & Jason Allan Tan & Ma. Regina Justina Estuar & Kennedy Espina, 2017. "Dengue Spread Modeling in the Absence of Sufficient Epidemiological Parameters: Comparison of SARIMA and SVM Time Series Models," Working papers Conference proceedings The Future of Ethics, Education and Research, October 16-17, 2017 22, Research Association for Interdisciplinary Studies.
    5. KUMAR Manoj & ANAND Madhu, 2014. "An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 9(1), pages 81-94, April.
    6. Meyler, Aidan, 1999. "A Statistical Measure Of Core Inflation," Research Technical Papers 2/RT/99, Central Bank of Ireland.
    7. Tamerlan Mashadihasanli, 2022. "Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 439-454, July.
    8. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    9. Jansson & M. & Trönnberg & C-C. & Hemlin & S., 2018. "The occurrence and importance of pension fund managers’ investment beliefs A web survey and critical incident study," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 7(4), pages 1-1.
    10. Quinn, Terry & Kenny, Geoff & Meyler, Aidan, 1999. "Inflation Analysis: An Overview," Research Technical Papers 1/RT/99, Central Bank of Ireland.
    11. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    12. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
    13. Aguilar, Ruben & Valdivia, Daney, 2011. "Precios de exportación de gas natural para Bolivia: Modelación y pooling de pronósticos [Bolivian natural gas export prices: Modeling and forecast pooling]," MPRA Paper 35485, University Library of Munich, Germany.
    14. Takatoshi Ito & Kazumasa Iwata & Colin McKenzie & Shujiro Urata, 2015. "Social Security in Ageing Asia: Editors' Overview," Asian Economic Policy Review, Japan Center for Economic Research, vol. 10(2), pages 179-198, July.
    15. Mohammad Rafiqul Islam & Nguyet Nguyen, 2020. "Comparison of Financial Models for Stock Price Prediction," JRFM, MDPI, vol. 13(8), pages 1-19, August.
    16. Shiying Tu & Jiehu Huang & Huailong Mu & Juan Lu & Ying Li, 2024. "Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast," Mathematics, MDPI, vol. 12(8), pages 1-15, April.
    17. Ntebogang Dinah Moroke, 2014. "The robustness and accuracy of Box-Jenkins ARIMA in modeling and forecasting household debt in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 6(9), pages 748-759.
    18. Cuenca, Janet S., 2016. "Social Protection in APEC: In Pursuit of Inclusive Growth," Research Paper Series DP 2016-03, Philippine Institute for Development Studies.
    19. S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
    20. Han Hwa Goh & Kim Leng Tan & Chia Ying Khor & Sew Lai Ng, 2016. "Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 323-344, December.

    More about this item

    Keywords

    ARIMA model; EPF; forecasting; long-term sustainability;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pdc:jrnbeh:v:14:y:2018:i:1:p:43-53. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jaroslav Holecek (email available below). General contact details of provider: https://edirc.repec.org/data/pradecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.