This study proposes di↵erent modelling approaches which exploit electricity market data to nowcast industrial production. Our models include linear, mixed-data sampling (MIDAS), Markov-Switching (MS) and MS-MIDAS regressions. Comparison against a commonly applied autoregressive approach shows that electricity market data significantly improves nowcasting performance especially during turbulent economic states characterised by high volatility and uncertainty, such as those generated by the recent COVID-19 pandemic. The most promising results are provided by MS models, which identify two regimes of di↵erent volatility. These results confirm that electricity market data provide timely and easy-to-access information for nowcasting macroeconomic variables, especially when it is most valuable, i.e. during times of crisis and uncertainty.

Keywords: Industrial production, Forecasting, Markov-switching, Mixed-data sampling, COVID-19.