An empirical examination of restructured electricity prices

By Christopher R. Knittel, Michael R. Roberts. 2005

470 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f8a6f54fe146a5c6e6f2389a2a1341d22183570b

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Citations in the corpus

Modelling dependence using skew t copulas: Bayesian inference and applications (2012, unknown): “Studies therefore focus on spot prices themselves; for example, see Knittel and Roberts (2005) and Koopman et al.”
The impact of wind power generation on the electricity price in Germany (2014, methodology): “The same holds for the Phillips-Perron test, employed by Knittel and Roberts (2005), with a test statistic of -17.37986 and a 1% critical value of -3.4330.”
Modeling and forecasting electricity spot prices: A functional data perspective (2013, background): “They differ substantially from time series of equities and other commodities because electricity still cannot be stored efficiently and, therefore, electricity demand has an untempered effect on the electricity spot price [Knittel and Roberts (2005)].”
Electricity markets around the world (2018, background): “…prices an interesting field for research and various studies have been conducted on modeling power prices, see, e.g. Lucia and Schwartz (2002); Weron et al. (2004b); Knittel and Roberts (2005); Bierbrauer et al. (2007); Seifert and Uhrig-Homburg (2007); Huisman et al. (2007), just to mention a few.”
Electricity prices, large-scale renewable integration, and policy implications (2017, background): “Hence, price variation was minimal and under the rigorous control of regulators (Knittel and Roberts, 2005).”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, unknown): “Knittel and Roberts (2005) evaluated an AR-EGARCH specification and found it superior to five other models during the crisis period (May 1, 2000 to August 31, 2000) in California.”
A stochastic dynamic programming model for co-optimization of distributed energy storage (2013, background): “Tests of historical price data from the California ISO and Singapore electricity markets show that energy price patterns best fit a log-normal distribution [21,30].”
Hourly Electricity Prices in Day-Ahead Markets (2007, background): “Knittel and Roberts (2005) fit a range of traditional financial models and less conventional electricity price models to an hourly time series of real-time Californian electricity prices, and find that forecasting performance of traditional models is poor and can be substantially improved when they…”