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.

Nowcasting industrial production using linear and non-linear models of electricity demand / Galdi, G.; Casarin, R.; Ferrari, D.; Fezzi, C.; Ravazzolo, F.. - In: ENERGY ECONOMICS. - ISSN 0140-9883. - 126:(2023), p. 107006. [10.1016/j.eneco.2023.107006]

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

Galdi G.
;
Fezzi C.;
2023-01-01

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.
2023
Galdi, G.; Casarin, R.; Ferrari, D.; Fezzi, C.; Ravazzolo, F.
Nowcasting industrial production using linear and non-linear models of electricity demand / Galdi, G.; Casarin, R.; Ferrari, D.; Fezzi, C.; Ravazzolo, F.. - In: ENERGY ECONOMICS. - ISSN 0140-9883. - 126:(2023), p. 107006. [10.1016/j.eneco.2023.107006]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/396689
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact