Time Series Analysis on Monthly Production of Crude Oil in Nigeria
DOI:
https://doi.org/10.70882/josrar.2024.v1i2.8Keywords:
Time Series, Crude oil Production, Auto-regressive Integrated Moving Average (ARIMA), Auto-correlation function (ACF), Partial Auto-correlation Function (PACF)Abstract
This study delves into a comprehensive time series analysis of the monthly production of crude oil, in Nigeria, a critical component of the country’s economy and a significant player in the global oil market. Understanding patterns, trends, and dynamics of crude oil production is essential for policymakers, industry stakeholders, and researchers to make informed decisions and forecasts. The research utilizes monthly secondary data on crude oil production in Nigeria, collected from the Nigeria National Petroleum Cooperation (NNPC) annual statistical bulletin 2010 and 2023 respectively, to explore various aspects of the time series, including seasonality, trends, and potential forecasting models. Minitab 17.0 was applied to run the data, advanced time series model, ARIMA (Auto-regressive Integrated Moving Average) was employed using the Box-Jenkins approach for crude oil production in Nigeria from January 1999 to June 2023 to forecast future production levels based on the historical data patterns. ARIMA (2,1,1) model was the best model fitted to the crude oil production data. The pattern showed that the model fitted for this study is adequate since the P-value can be seen from table 2 is greater than 0.05. The result indicates that the forecasted values of crude oil production fluctuate steadily.
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