Editor:
Universidad Carlos III de Madrid. Departamento de Estadística
Issued date:
2017-01
ISSN:
2387-0303
Sponsor:
Acknowledgements: A.M. Alonso acknowledges support of the Spanish Ministry of Economy and
Competitiveness, research projects ECO2012-38442, and ECO2015-66593. Carolina García-Martos
acknowledges financial support from project DPI2011-23500, Spanish Ministry of Economy and
Competitiveness.
The authors would like to extend their appreciation to Professor Michael Wiper for his assistance and
corrections regarding the proper use of English in this document.
Serie/No.:
UC3M Working papers. Statistics and Econometrics 17-01
Project:
Gobierno de España. ECO2012-38442 Gobierno de España. ECO2015-66593 Gobierno de España. DPI2011-23500
Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
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 taskIn 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 can not 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 acombination of models for the factors
than by a single model. Although our procedure is applicable in other markets, it is
illustrated with applications to forecasting spot prices of the Iberian Market, MIBEL
(The Iberian Electricity Market) and the Italian Market. Three combinations of
forecasts are successful in providing improved results for alternative forecasting
horizons.[+][-]