26/09/2026
π CASE STUDY | Natural Gas Price Forecasting Using EViews
How can statistical modelling help us understand and forecast volatile energy prices? β½π
In this study, TRPC Natural Gas prices from 2011β2025 were analyzed using OLS, VAR, ARIMA, SARIMA and GARCH models.
π What did the analysis reveal?
Natural Gas and Coal showed a very strong relationship (r = 0.90). The initial OLS model achieved RΒ² = 0.82, but diagnostic tests identified autocorrelation and heteroskedasticity, showing why model diagnostics are essential before relying on regression results.
A significant structural break around March 2020 was also detected, indicating a major change in the underlying price relationships.
π Different forecasting models were then compared. ARIMA and SARIMA achieved MAPE values around 4%, while GARCH produced considerably higher forecast errors in this analysis.
π― Key takeaway:
A high RΒ² alone does not guarantee a reliable forecasting model. Diagnostics, model selection and forecast evaluation are essential for meaningful research.
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