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Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy

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Short-term electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the literature does not provide guidelines on how to select the optimal 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 (Nord Pool and IPEX) with a selection of eleven different forecasting models. Incidentally, our empirical application reveals that simple models, such as a simple linear regression (SLR) with only two parameters, can perform unexpectedly well if estimated on extremely short samples. Surprisingly, in the Nord Pool, such SLR is the best performing model in 13 out 24 trading periods.

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Keywords: Electricity price forecasting, Day-ahead market, Parameter instability, Bandwidth selection, Statistical models, Artificial neural networks.

DOI: 10.5547/01956574.41.4.cfez

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Published in Volume 41, Number 4 of the bi-monthly journal of the IAEE's Energy Economics Education Foundation.


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