Arbeitspapier
Forecasting daily electricity prices with monthly macroeconomic variables
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.
- ISBN
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978-92-899-3512-8
- Language
-
Englisch
- Bibliographic citation
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Series: ECB Working Paper ; No. 2250
- Classification
-
Wirtschaft
Bayesian Analysis: General
Forecasting Models; Simulation Methods
Energy and the Macroeconomy
Energy Forecasting
- Subject
-
Density Forecasting
Electricity Prices
Forecasting
Mixed-Frequency VAR models
MIDAS models
- Event
-
Geistige Schöpfung
- (who)
-
Foroni, Claudia
Ravazzolo, Francesco
Rossini, Luca
- Event
-
Veröffentlichung
- (who)
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European Central Bank (ECB)
- (where)
-
Frankfurt a. M.
- (when)
-
2019
- DOI
-
doi:10.2866/341253
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Foroni, Claudia
- Ravazzolo, Francesco
- Rossini, Luca
- European Central Bank (ECB)
Time of origin
- 2019
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