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

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

Energy Economics, 2023, vol. 126, issue C

Abstract: This article 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. Comparisons against autoregressive approaches and other commonly used macroeconomic predictors show that electricity market data combined with an MS model significantly improve nowcasting performance, especially during turbulent economic states, such as those generated by the recent COVID-19 pandemic. The most promising results are provided by an MS model which identifies two volatility regimes. 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 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Nowcasting industrial production using linear and non-linear models of electricity demand (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:126:y:2023:i:c:s0140988323005042

DOI: 10.1016/j.eneco.2023.107006

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