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Forecasting electricity prices through robust nonlinear models

Author

Listed:
  • Luigi Grossi

    (Department of Economics (University of Verona))

  • Fany Nan

    (Joint Research Centre of EU (Ispra))

Abstract

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models. In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for GM-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function on the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.

Suggested Citation

  • Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
  • Handle: RePEc:ver:wpaper:06/2017
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    Keywords

    Electricity price; Nonlinear time series; Price forecasting; Robust GM-stimator; Spikes; Threshold models;
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