Abstract
This study applies a Vector Autoregressive (VAR) model to forecast oil product prices in Nigeria, specifically petrol (PMS), diesel (AGO), kerosene (NHK), and liquefied petroleum gas (LPG) using data from January 2016 to March 2024. Nigeria's economy is highly sensitive to oil price fluctuations, driven by both domestic and global factors. The VAR model of order 2 effectively captures the interdependencies among the prices of these products and all the equations show very high R-squared values with AGO and NHK performing the best with 99%, followed by LPG and PMS with 98% and 96% respectively. Results reveal significant interrelations and predict an upward trend in oil product prices over the forecast period. The model’s predictive accuracy was validated through Root Mean Squared Error (RMSE), 85.83002 for PMS, 213.9482 for AGO, 63.92084 for NHK and 174.9873 for LPG and Mean Absolute Error (MAE) metrics, 76.1557 for PMS, 187.8595 for AGO, 52.4654 for NHK and 151.1315 for LPG. In light of these findings, it is recommended that policymakers adopt strategic measures, such as improving real-time monitoring and building strategic reserves, to stabilize prices and manage economic impacts.
Keywords
Oil product Prices, Vector Autoregression, Autocorrelation, Root Mean Squared Error, Volatility.
Citations
IRE Journals:
Abdulsomad O. Olaitan, Rasheed A. Adeyemi "Vector Autoregressive Model of Crude Oil Product Prices in Nigeria" Iconic Research And Engineering Journals Volume 9 Issue 4 2025 Page 1350-1360 https://doi.org/10.64388/IREV9I4-1711497-7240
IEEE:
Abdulsomad O. Olaitan, Rasheed A. Adeyemi
"Vector Autoregressive Model of Crude Oil Product Prices in Nigeria" Iconic Research And Engineering Journals, 9(4) https://doi.org/10.64388/IREV9I4-1711497-7240
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12