Component estimation for electricity prices: Procedures and comparisons

Electricity price time series usually exhibit some form of nonstationarity, corresponding to long-term behavior, one or more periodic components as well as dependence on calendar effects. As a result, modeling electricity prices requires accounting for both long-term and periodic components. In the...

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Bibliographic Details
Published in:Energy economics 2014-07, Vol.44, p.143-159
Main Authors: Lisi, Francesco, Nan, Fany
Format: Article
Language:English
Subjects:
Accounting
Applied sciences
Calendars
Comparative analysis
Component estimation
Economic data
Economics
Electric energy
Electric power
Electric rates
Electricity prices
Electricity pricing
Energy
Energy economics
Energy prices
Estimating techniques
Estimation
Exact sciences and technology
Filtering
Filtering procedures
Filtration
General, economic and professional studies
Long-term analysis
Long-term dynamics
Markets
Mathematical models
Methodology. Modelling
Modelling
Nonparametric methods
Prices
Studies
Time series
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Summary:Electricity price time series usually exhibit some form of nonstationarity, corresponding to long-term behavior, one or more periodic components as well as dependence on calendar effects. As a result, modeling electricity prices requires accounting for both long-term and periodic components. In the literature, several filtering procedures have been proposed but a standard has not yet been found. Furthermore, since different procedures are applied in contexts that are not homogeneous with respect to data, periods and final goals, a fair comparison is difficult. This work considers several methods for component estimation in a homogeneous framework and compares them according to specific criteria. The final purpose is to find an estimation procedure that performs well, independently of the intended market and that can be proposed as a reference for electricity price time series filtering. •We compare methods for component estimation in electricity price time series.•11 methods for long-run dynamics and 3 for periodic behavior are considered.•3 criteria for filtering evaluation are proposed.•We find that a procedure based on smoothing splines and trimmed mean is the best for component estimation.
ISSN:0140-9883
1873-6181
DOI:10.1016/j.eneco.2014.03.018