Forecasting daily electricity prices with monthly macroeconomic variables
Francesco Ravazzolo and
No 2250, Working Paper Series from European Central Bank
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)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20192250
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