Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/208284 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
ECB Working Paper No. 2250
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Density Forecasting
Electricity Prices
Forecasting
Mixed-Frequency VAR models
MIDAS models
JEL: 
C11
C53
Q43
Q47
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-899-3512-8
Dokumentart: 
Working Paper

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