Abstract

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX (''X'' stands for exogenous/fundamental variable -– system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-of-sample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting.

Recommended Citation

Adam Misiorek, Stefan Trueck, and Rafal Weron (2006) "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models", Studies in Nonlinear Dynamics & Econometrics: Vol. 10: No. 3, Article 2.
http://www.bepress.com/snde/vol10/iss3/art2

Related Files

misiorek_datacode.zip (304 kB)
Data and code