  Recent research finds that forecasting electricity prices is very relevant.
In many applications, it might be interesting to predict daily electricity
prices by using their own lags or renewable energy sources. However, the recent
turmoil of energy prices and the Russian-Ukrainian war increased attention in
evaluating the relevance of industrial production and the Purchasing Managers'
Index output survey in forecasting the daily electricity prices. We develop a
Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in
frequency between the daily prices and the monthly macro variables in Germany
and Italy. We find that the inclusion of macroeconomic low frequency variables
is more important for short than medium term horizons by means of point and
density measures. In particular, accuracy increases by combining hard and soft
information, while using only surveys gives less accurate forecasts than using
only industrial production data.
