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

Abstract

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.JEL Classifications: E27, G23, H55

Mathematics Model on Syariah Share Pricing in ISSI

“…Pemodelan tersebut berdasarkan pada data time series yang diperoleh dari hasil pengamatan Vegetation Temperature Condition Index (VTCI) (Tian et al, 2016). Penerapan lain metode ini adalah pada data keberlanjutan dana providet karyawan di Malaysia (Hassan & Othman, 2018). Bahkan Autoregressive Integrated Moving Average-based Distributed Predictive Tracking (ARIMA-DPT) dapat menampilkan model yang akurat untuk memprediksikan target lokasi menggunakan data time series sampai pada tahapan evaluasi signifikansi model prediksi yang telah disajikan (Banaezadeh & Haghighat, 2015).…”

Forecasting of the unknown end-of-life tire flow for control and decision making in urban solid waste management: A case study

“…Two models (linear and non-linear) were used to predict the NC flows of ELT for 12 months ahead. The first one comprised the use of the ARIMA model structure (Hassan and Othman, 2018) and the second comprised a FeedForward Artificial Neural Network (FFANN) (Adamović et al, 2018; Singh et al, 2018). The results obtained by both models were compared in order to verify similarity in predicted dynamic behavior.…”