Nowcasting industrial production using linear and non-linear models of electricity demand

This study proposes different 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 signif- icantly 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 different volatility. These results confirm that electricity mar- ket 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.