Size matters: Estimation sample length and electricity price forecasting accuracy

Carlo Fezzi () and Luca Mosetti

No 2018/10, DEM Working Papers from Department of Economics and Management

Abstract: Electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the current literature does not provide much guidance on how to select the size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets with a selection of ten different forecasting models. Incidentally, our empirical application reveals that simple models, such as the linear regression, can perform surprisingly well if estimated on extremely short samples.

Keywords: electricity price forecasting; day-ahead market; parameter instability; bandwidth selection; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C22 C45 C51 C53 Q47 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ene, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.economia.unitn.it/alfresco/download/wo ... 59/DEM2018_10NEW.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:trn:utwprg:2018/10

Access Statistics for this paper

More papers in DEM Working Papers from Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by roberto.gabriele@unitn.it ().