A generalized exponential time series regression model for electricity prices

Niels Haldrup (), Oskar Knapik () and Tommaso Proietti
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Oskar Knapik: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We consider the issue of modeling and forecasting daily electricity spot prices on the Nord Pool Elspot power market. We propose a method that can handle seasonal and non-seasonal persistence by modelling the price series as a generalized exponential process. As the presence of spikes can distort the estimation of the dynamic structure of the series we consider an iterative estimation strategy which, conditional on a set of parameter estimates, clears the spikes using a data cleaning algorithm, and reestimates the parameters using the cleaned data so as to robustify the estimates. Conditional on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better strategy than forecasting the daily average directly.

Keywords: Robust estimation; long-memory; seasonality; electricity spot prices; Nord Pool power market; forecasting; robust Kalman lter; generalized exponential model (search for similar items in EconPapers)
JEL-codes: C1 C5 C53 Q4 (search for similar items in EconPapers)
Pages: 24
Date: 2016-03-18
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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