Abstract
Using data for the Nord Pool power grid, we derive a space-time Durbin model for electricity spot prices with both temporal and spatial lags. Joint modeling of temporal and spatial adjustment effects is necessarily important when prices and loads are determined in a network grid. By using different spatial weight matrices, statistical tests show significant spatial dependence in the spot price dynamics across areas. In fact, estimation of the model shows that the spatial dependence is as important as the temporal dependence in describing the spot price dynamics. We decompose price impacts into direct and indirect effects and demonstrate how price effects transmit to neighboring markets and decline with distance. A forecasting comparison with a non-spatial model shows that the space-time model improves forecasting performance for 7 and 30 days ahead forecasts. A model with time-varying parameters is estimated for an expanded sample period and it is found that the spatial correlation within the power grid has increased over time. We interpret this to indicate an increasing degree of market integration within the sample period.
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