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

Giulio Galdi, Roberto Casarin, Davide Ferrari, Carlo Fezzi () and Francesco Ravazzolo

No 2022/2, DEM Working Papers from Department of Economics and Management

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.

Date: 2022
New Economics Papers: this item is included in nep-ene
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Journal Article: Nowcasting industrial production using linear and non-linear models of electricity demand (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:trn:utwprg:2022/2

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