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World steel production: A new monthly indicator of global real economic activity

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  • Francesco Ravazzolo
  • Joaquin Vespignani

Abstract

In this paper we propose a new indicator of monthly global real economic activity, named world steel production. We use world steel production, OECD industrial production index and Kilian’s rea index to forecast world real GDP, and key commodity prices. We find that world steel production generates large statistically significant gains in forecasting world real GDP and oil prices, relative to an autoregressive benchmark. A forecast combination of the three indices produces statistically significant gains in forecasting world real GDP, oil, natural gas, gold and fertilizer prices, relative to an autoregressive benchmark.

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  • Francesco Ravazzolo & Joaquin Vespignani, 2017. "World steel production: A new monthly indicator of global real economic activity," CAMA Working Papers 2017-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2017-42
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    Cited by:

    1. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    2. Olayeni, Olaolu Richard & Tiwari, Aviral Kumar & Wohar, Mark E., 2020. "Global economic activity, crude oil price and production, stock market behaviour and the Nigeria-US exchange rate," Energy Economics, Elsevier, vol. 92(C).
    3. Funashima, Yoshito, 2020. "Global economic activity indexes revisited," Economics Letters, Elsevier, vol. 193(C).
    4. Diaz, Elena Maria & Pérez Quirós, Gabriel, 2020. "Daily tracker of global economic activity: a close-up of the COVID-19 pandemic," Working Paper Series 2505, European Central Bank.
    5. Angelini, Giovanni & Cavaliere, Giuseppe & Fanelli, Luca, 2024. "An identification and testing strategy for proxy-SVARs with weak proxies," Journal of Econometrics, Elsevier, vol. 238(2).
    6. 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).
    7. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    8. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    9. Diaz, Elena Maria & Perez-Quiros, Gabriel, 2021. "GEA tracker: A daily indicator of global economic activity," Journal of International Money and Finance, Elsevier, vol. 115(C).
    10. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
    11. Arabinda Basistha & Richard Startz, 2024. "Measuring persistent global economic factors with output, commodity price, and commodity currency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2860-2885, November.
    12. Wen, Xiaoqian & Xie, Yuxin & Pantelous, Athanasios A., 2022. "Extreme price co-movement of commodity futures and industrial production growth: An empirical evaluation," Energy Economics, Elsevier, vol. 108(C).
    13. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    14. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    15. Jiménez-Rodríguez, Rebeca, 2022. "Oil shocks and global economy," Energy Economics, Elsevier, vol. 115(C).
    16. Afees A. Salisu & Philip C. Omoke & Abdulsalam Abidemi Sikiru, 2023. "Geopolitical risk and global financial cycle: Some forecasting experiments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 3-16, January.
    17. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    18. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    19. Chew Lian Chua & Sarantis Tsiaplias & Ruining Zhou, 2024. "Constructing a high‐frequency World Economic Gauge using a mixed‐frequency dynamic factor model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2212-2227, September.
    20. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie & Ji, Qiang, 2020. "The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach," Research in International Business and Finance, Elsevier, vol. 54(C).
    21. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.

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    More about this item

    Keywords

    Global real economic activity; World steel production; Forecasting;
    All these keywords.

    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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