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A New Monthly Indicator of Global Real Economic Activity

Author

Listed:
  • Francesco Ravazzolo

    (Norges Bank (Central Bank of Norway) and BI Norwegian Business School)

  • Joaquin L. Vespignani

    (University of Tasmania, Tasmanian School of Business and Economics and Centre for Applied Macroeconomic Analysis, Australia)

Abstract

In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian's index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian's index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-of-sample exercise, both Kilian's index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark.

Suggested Citation

  • Francesco Ravazzolo & Joaquin L. Vespignani, 2015. "A New Monthly Indicator of Global Real Economic Activity," Working Paper 2015/06, Norges Bank.
    • Handle: RePEc:bno:worpap:2015_06
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    Cited by:

    1. Wang, Quan-Jing & Feng, Gen-Fu & Chen, Yin E. & Wen, Jun & Chang, Chun-Ping, 2019. "The impacts of government ideology on innovation: What are the main implications?," Research Policy, Elsevier, vol. 48(5), pages 1232-1247.
    2. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    3. Nooman Rebei & Rashid Sbia, 2021. "Transitory and permanent shocks in the global market for crude oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 1047-1064, November.
    4. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    5. Baumann, Ursel & Gómez Salvador, Ramón & Seitz, Franz, 2018. "Global recessions and booms: What do probit models tell us?," Weidener Diskussionspapiere 61, University of Applied Sciences Amberg-Weiden (OTH).
    6. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    7. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    8. repec:bny:wpaper:0074 is not listed on IDEAS
    9. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    10. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    11. Víctor Riquelme & Gabriela Riveros, 2018. "Un Indicador Contemporáneo de Actividad (ICA) para Chile," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 21(1), pages 134-149, April.
    12. Miao, Hong & Ramchander, Sanjay & Wang, Tianyang & Yang, Dongxiao, 2017. "Influential factors in crude oil price forecasting," Energy Economics, Elsevier, vol. 68(C), pages 77-88.
    13. Sek, Siok Kun, 2019. "Unveiling the factors of oil versus non-oil sources in affecting the global commodity prices: A combination of threshold and asymmetric modeling approach," Energy, Elsevier, vol. 176(C), pages 272-280.
    14. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    15. Saleh Mothana Obadi & Kristina Gardonova, 2019. "How does the Production of Unconventional Resources of Energy Influence Energy Security: Empirical Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 46-54.
    16. Amar Rao & Marco Tedeschi & Kamel Si Mohammed & Umer Shahzad, 2024. "Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3295-3315, December.
    17. Rausser, Gordon & Stuermer, Martin, 2020. "A Dynamic Analysis of Collusive Action: The Case of the World Copper Market, 1882-2016," MPRA Paper 104708, University Library of Munich, Germany.
    18. Mr. Sohrab Rafiq, 2016. "When China Sneezes Does ASEAN Catch a Cold?," IMF Working Papers 2016/214, International Monetary Fund.

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    Keywords

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    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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