LAPSE:2019.0105
Published Article
LAPSE:2019.0105
Electricity Price Forecasting by Averaging Dynamic Factor Models
January 7, 2019
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
Bayesian model averaging, dimensionality reduction, electricity prices, forecast combination
Suggested Citation
Alonso AM, Bastos G, García-Martos C. Electricity Price Forecasting by Averaging Dynamic Factor Models. (2019). LAPSE:2019.0105
Author Affiliations
Alonso AM: Department of Statistics, Universidad Carlos III de Madrid, Getafe 28903, Madrid, Spain; Instituto Flores de Lemus, Universidad Carlos III de Madrid, Getafe 28903, Madrid, Spain [ORCID]
Bastos G: Department of Statistics, Universidad Carlos III de Madrid, Getafe 28903, Madrid, Spain
García-Martos C: Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid 28040, Spain [ORCID]
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Journal Name
Energies
Volume
9
Issue
8
Article Number
E600
Year
2016
Publication Date
2016-07-28
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9080600, Publication Type: Journal Article
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LAPSE:2019.0105
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doi:10.3390/en9080600
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Jan 7, 2019
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CC BY 4.0
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Jan 7, 2019
 
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Calvin Tsay
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