This paper forecasts station-level retail fuel prices using econometric methods, incorporating spatial interdependencies. Error correction models with cross-sectional dependence outperform autoregressive models with wholesale prices or spatial effects, demonstrating the benefits of spatial interdependencies in terms of improved forecasting performance.
