Electricity Price Forecasting by Averaging Dynamic Factor Models

Andrés M. Alonso, Guadalupe Bastos and Carolina García-Martos
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Andrés M. Alonso: Department of Statistics, Universidad Carlos III de Madrid, Getafe 28903, Madrid, Spain
Guadalupe Bastos: Department of Statistics, Universidad Carlos III de Madrid, Getafe 28903, Madrid, Spain
Carolina García-Martos: Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid 28040, Spain

Energies, 2016, vol. 9, issue 8, 1-21

Abstract: In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.

Keywords: dimensionality reduction; electricity prices; Bayesian model averaging; forecast combination (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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