Modeling and Forecasting Crude Oil Prices in Nigeria Using ARIMA: A Time Series Analysis from 2013-2022
DOI:
https://doi.org/10.56919/usci.2433.033Keywords:
Data, Forecasting, Model, Oil, Selection, TrendAbstract
Study’s Excerpt/Novelty
- This study employs the Box-Jenkins ARIMA model to forecast crude oil prices in Nigeria from 2013 to 2022, identifying the ARIMA (1, 1, 1) model as the most suitable based on AIC, BIC, and HQIC criteria.
- The findings emphasize the model’s adequacy for short-term price forecasting but highlight its limitations, particularly its sensitivity to non-stationary data.
- The study recommends incorporating macroeconomic variables or hybrid models for more accurate long-term predictions, while urging Nigerian oil sector stakeholders to explore alternatives like Liquefied Natural Gas (LNG) to mitigate revenue losses from declining crude oil sales.
Full Abstract
This study utilizes Box-Jenkins ARIMA modeling to forecast crude oil prices in Nigeria from 2013 to 2022. The data, sourced from the CBN statistical bulletin, revealed non-stationarity, corrected by first differencing. The ARIMA (1, 1, 1) model was identified as the most suitable based on AIC, BIC, and HQIC criteria, providing significant information on short-term price forecasting and model adequacy. The study concludes that while ARIMA models are effective for short-term forecasting, their limitations, such as sensitivity to non-stationary data, suggest that future research should incorporate macroeconomic variables or hybrid models for improved accuracy. It was recommended that stakeholders in the Nigerian oil sector should explore other avenues in the sector, such as Liquefied Natural Gas(LNG), in order to absorb the decrease in revenue as a result of the decline in revenue from crude oil prices and sales.
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