The Impact of RON95 Petrol Prices on Inflation in Malaysia: A Vector Autoregressive (VAR) Approach
DOI:
https://doi.org/10.22452/Keywords:
Consumer Price Index, RON95 petrol, fuel prices, time series analysis, VAR modelAbstract
The Consumer Price Index (CPI) is a key measure of inflation, and in Malaysia, petrol prices are included as a fixed component within the CPI basket. Amid the global oil price volatility and geopolitical tensions, understanding the impact of fuel price shocks on inflation has become increasingly relevant. During two distinct periods, from November 2014 to January 2018 and from March 2020 to February 2021, the price of RON95 petrol, Malaysia’s primary fuel, was determined by market forces rather than government regulation. These periods offer a natural setting to examine how RON95 price fluctuations influence domestic inflation. This study examines the relationship between Malaysia's Consumer Price Index (CPI) and RON95 petrol prices using monthly data from November 2014 to June 2021, employing a Vector Autoregressive (VAR) model to explain the dynamic relationship between the two variables. The results indicate that the short-term relationship between the CPI and RON95 petrol prices varies across lags, exhibiting both positive and negative effects, with a mixed correlation. Impulse response analysis reveals that changes in RON95 prices generate a positive response in CPI in both the short and long term. Variance decomposition confirms the predictive relevance of RON95 prices for CPI variation. The variance decomposition results further support this pattern, showing that the share of CPI forecast error variance attributed to RON95 shocks increases from 31.07% in the first period to approximately 63.47% by the tenth period, while inflation accounts for 31.97% in the fourth period to approximately 28.45% by the tenth period. These findings provide empirical evidence that can inform proactive economic measures, such as supporting Bank Negara Malaysia to adjust its monetary policy in response to fuel price fluctuations and may contribute to improved inflation forecasting in future applications.





