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Combining day-ahead forecasts for British electricity prices

Author

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  • Bordignon, Silvano
  • Bunn, Derek W.
  • Lisi, Francesco
  • Nan, Fany

Abstract

This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

Suggested Citation

  • Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
  • Handle: RePEc:eee:eneeco:v:35:y:2013:i:c:p:88-103
    DOI: 10.1016/j.eneco.2011.12.001
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasts combination; Prediction accuracy; ARMAX; Time-varying parameter regression; Markov regime switching; Electricity price forecasting;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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