We investigate the effects of outlier treatment on the estimation of the seasonal component and stochastic models in electricity markets. Typically, electricity spot prices exhibit features like seasonality, mean-reverting behavior, extreme volatility and the occurrence of jumps and spikes. Hence, an important issue in the estimation of stochastic models for electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, in regression analysis, classical estimation routines like OLS are very sensitive to extreme observations and outliers. Improved robustness of the model can be achieved by (a) cleaning the data with some reasonable procedure for outlier rejection, and then (b) using classical estimation and testing procedures on the remainder of the data. We examine the effects on model estimation for different treatment of extreme observations in particular on determining the number of outliers and descriptive statistics of the remaining series after replacement of the outliers. Our findings point out the substantial impact the treatment of extreme observations may have on these issues.
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number
4711.
Find related papers by JEL classification: Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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Huisman, R. & Mahieu, R.J., 2001.
"Regime Jumps in Electricity Prices,"
Research Paper
ERS-2001-48-F&A Revision_, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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