Vector Autoregressive Model of Crude Oil Product Prices in Nigeria

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@article{1711497,
      author = {Abdulsomad O. Olaitan, Rasheed A. Adeyemi},
      title = {Vector Autoregressive Model of Crude Oil Product Prices in Nigeria},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {1350-1360},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711497.pdf},
      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.},
      month = {October},
      doi = {https://doi.org/10.64388/IREV9I4-1711497-7240}
  }