Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/208284
Authors: 
Foroni, Claudia
Ravazzolo, Francesco
Rossini, Luca
Year of Publication: 
2019
Series/Report no.: 
ECB Working Paper 2250
Abstract: 
We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forecasting accuracy for different horizons (from 1 day ahead to 28 days ahead) and by considering different specifications of the models. We find gains around 20% at short horizons and around 10% at long horizons. Therefore, it turns out that the macroeconomic low frequency variables are more important for short horizons than for longer horizons. The benchmark is almost never included in the model confidence set.
Subjects: 
Density Forecasting
Electricity Prices
Forecasting
Mixed-Frequency VAR models
MIDAS models
JEL: 
C11
C53
Q43
Q47
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-3512-8
Document Type: 
Working Paper

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