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α: calibrated so average coauthorship-adjusted count equals average raw count
Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine-based models. However, a clear disadvantage of the wavelet-based approach is the increased complexity of the technique, as compared to the other two classes of LTSC models, and the resulting need for dedicated numerical software, which may not be readily available to practitioners in their work environments. To facilitate this problem, we propose here a much simpler, yet equally powerful method for identifying the LTSC in electricity spot price series. It makes use of the Hodrick–Prescott (HP) filter, a widely-recognized tool in macroeconomics.