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Nowcasting Italian industrial production: the predictive role of lubricant oils

Author

Listed:
  • Marco Fruzzetti

    (Bank of Italy)

  • Tiziano Ropele

    (Bank of Italy)

Abstract

This paper examines the potential of industrial lubricant oils as a predictor for nowcasting the index of Italian industrial production. The results show that nowcast accuracy can be significantly enhanced during periods of economic turbulence, such as the 2021-22 energy crisis. Industrial lubricant oils are a more robust economic indicator than other commonly used energy-related timely predictors, such as industrial gas consumption. Furthermore, the findings may prove relevant for nowcasting industrial production in the process of structural changes, such as the ongoing green transition.

Suggested Citation

  • Marco Fruzzetti & Tiziano Ropele, 2024. "Nowcasting Italian industrial production: the predictive role of lubricant oils," Questioni di Economia e Finanza (Occasional Papers) 866, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_866_24
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0866/QEF_866_24.pdf
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    References listed on IDEAS

    as
    1. Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    2. De Santis, Roberto A. & Tornese, Tommaso, 2023. "Energy supply shocks’ nonlinearities on output and prices," Working Paper Series 2834, European Central Bank.
    3. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    4. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    5. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    6. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    7. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    8. Valentina Aprigliano, 2020. "A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1291-1304, December.
    9. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    10. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    11. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    12. Matteo Alpino & Luca Citino & Annalisa Frigo, 2023. "The effects of the 2021 energy crisis on medium-sized and large industrial firms: evidence from Italy," Questioni di Economia e Finanza (Occasional Papers) 776, Bank of Italy, Economic Research and International Relations Area.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Francesco Corsello & Marco Flaccadoro & Stefania Villa, 2023. "Quantity versus price dynamics: the role of energy and bottlenecks in the Italian industrial sector," Questioni di Economia e Finanza (Occasional Papers) 781, Bank of Italy, Economic Research and International Relations Area.
    15. Chiacchio, Francesco & De Santis, Roberto A. & Gunnella, Vanessa & Lebastard, Laura, 2023. "How have higher energy prices affected industrial production and imports?," Economic Bulletin Boxes, European Central Bank, vol. 1.
    16. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    nowcasting; industrial production; energy; lubricants;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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