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

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Listed:
  • Giulio Galdi
  • Roberto Casarin
  • Davide Ferrari
  • Carlo Fezzi
  • Francesco Ravazzolo

Abstract

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.

Suggested Citation

  • Giulio Galdi & Roberto Casarin & Davide Ferrari & Carlo Fezzi & Francesco Ravazzolo, 2022. "Nowcasting industrial production using linear and non-linear models of electricity demand," DEM Working Papers 2022/2, Department of Economics and Management.
    • Handle: RePEc:trn:utwprg:2022/2
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