Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Weron, Rafal and Misiorek, Adam (2008): Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. Forthcoming in: International Journal of Forecasting

Abstract

This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models, their extensions – spike preprocessed, threshold and semiparametric autoregressions (i.e. AR models with nonparametric innovations), as well as, mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.

• Item Type: MPRA Paper
• Language: English
• Keywords: Electricity market, Price forecast, Autoregressive model, Nonparametric maximum likelihood, Interval forecast, Conditional coverage
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q40 - General

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