Comparative Revenue Forecasting of GST vs SST in Malaysia: Time Series and Regression Analysis

Zulhaimi Bin Daud

School of Quantitative Sciences, Universiti Utara Malaysia, 06010 UUM, Sintok, Kedah, Malaysia.

Muhammad Bin Mat Yusof *

School of Quantitative Sciences, Universiti Utara Malaysia, 06010 UUM, Sintok, Kedah, Malaysia.

*Author to whom correspondence should be addressed.


Abstract

The evolution of Malaysia’s indirect tax framework is defined by three major regimes consisting of the single-stage Sales and Service Tax (SST 1.0), the multi-stage Goods and Services Tax (GST) introduced in 2015, and the reinstated SST 2.0 in 2018. This study utilizes a methodological triangulation approach to quantify the long-term revenue implications and fiscal opportunity costs associated with these shifts. Holt-Winters and ARIMA models were applied to analyse long-run historical trends (SST 1.0 and GDP), while bivariate regression models were utilized to forecast the more recent, albeit data-constrained, GST and SST 2.0 series. Utilizing quarterly data, the analysis explicitly acknowledges limited sample sizes for the GST (n=14) and SST 2.0 (n=22) periods. Findings reveal a significant and widening fiscal gap, by Q4 2028, a continued GST regime is projected to generate RM21.8 billion quarterly, effectively double the RM10.9 billion projected yield of SST 2.0, representing a quarterly fiscal opportunity cost of RM10.9 billion. The study identifies a “Predictability-Potency Paradox,” where SST 2.0 exhibits greater budgetary predictability R² = .601), whereas GST demonstrates superior revenue potency and buoyancy (β = 0.048). These results underscore a critical “Policy Trilemma” for Malaysian policymakers, necessitating a complex trade-off between fiscal maximization, political acceptability, and administrative simplicity.

Keywords: Indirect tax, GST vs SST, revenue forecasting, time series analysis, regression analysis


How to Cite

Daud, Zulhaimi Bin, and Muhammad Bin Mat Yusof. 2026. “Comparative Revenue Forecasting of GST Vs SST in Malaysia: Time Series and Regression Analysis”. Asian Journal of Probability and Statistics 28 (1):58-70. https://doi.org/10.9734/ajpas/2026/v28i1854.

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