Forecasting daily electricity prices with monthly macroeconomic variables

Claudia Foroni, Francesco Ravazzolo and Luca Rossini

No 2250, Working Paper Series from European Central Bank

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. JEL Classification: C11, C53, Q43, Q47

Keywords: density forecasting; electricity prices; forecasting; MIDAS models; mixed-frequency VAR models (search for similar items in EconPapers)
Date: 2019-03
New Economics Papers: this item is included in nep-ene and nep-for
Note: 3243564
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20192250

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