IDEAS home Printed from https://ideas.repec.org/p/een/camaaa/2017-42.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-06/42_2017_ravazzolo_vespignani.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ron Alquist & Olivier Gervais, 2013. "The Role of Financial Speculation in Driving the Price of Crude Oil," The Energy Journal, , vol. 34(3), pages 35-54, July.
    2. Angus Deaton, 2005. "Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 1-19, February.
    3. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    4. Humphreys, David, 2010. "The great metals boom: A retrospective," Resources Policy, Elsevier, vol. 35(1), pages 1-13, March.
    5. Christiane Baumeister & Gert Peersman, 2013. "The Role Of Time‐Varying Price Elasticities In Accounting For Volatility Changes In The Crude Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1087-1109, November.
    6. Jan J. J. Groen & Paolo A. Pesenti, 2011. "Commodity Prices, Commodity Currencies, and Global Economic Developments," NBER Chapters, in: Commodity Prices and Markets, pages 15-42, National Bureau of Economic Research, Inc.
    7. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    8. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
    9. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2012. "Oil prices, exchange rates and emerging stock markets," Energy Economics, Elsevier, vol. 34(1), pages 227-240.
    10. Christiane Baumeister & Lutz Kilian, 2016. "Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 139-160, Winter.
    11. Matteo Ciccarelli & Benoît Mojon, 2010. "Global Inflation," The Review of Economics and Statistics, MIT Press, vol. 92(3), pages 524-535, August.
    12. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    13. Grilli, Vittorio & Roubini, Nouriel, 1996. "Liquidity models in open economies: Theory and empirical evidence," European Economic Review, Elsevier, vol. 40(3-5), pages 847-859, April.
    14. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    15. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    16. Victor Zarnowitz, 1987. "The Regularity of Business Cycles," NBER Working Papers 2381, National Bureau of Economic Research, Inc.
    17. Gorton, Gary, 1988. "Banking Panics and Business Cycles," Oxford Economic Papers, Oxford University Press, vol. 40(4), pages 751-781, December.
    18. Francesco Lippi & Andrea Nobili, 2012. "Oil And The Macroeconomy: A Quantitative Structural Analysis," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1059-1083, October.
    19. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    20. Shaohua Chen & Martin Ravallion, 2010. "The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(4), pages 1577-1625.
    21. Ratti, Ronald A & Vespignani, Joaquin L., 2012. "Why are crude oil prices high when global activity is weak?," MPRA Paper 43777, University Library of Munich, Germany.
    22. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "What Drives Oil Prices? Emerging Versus Developed Economies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1013-1028, November.
    23. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    24. Mario Crucini & Ayhan Kose & Christopher Otrok, 2011. "What are the driving forces of international business cycles?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 156-175, January.
    25. M. Ayhan Kose & Christopher Otrok & Eswar Prasad, 2012. "Global Business Cycles: Convergence Or Decoupling?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 511-538, May.
    26. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    27. Apergis, Nicholas & Miller, Stephen M., 2009. "Do structural oil-market shocks affect stock prices?," Energy Economics, Elsevier, vol. 31(4), pages 569-575, July.
    28. Charles Steindel, 2004. "The relationship between manufacturing production and goods output," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 10(Aug).
    29. Radetzki, Marian, 2006. "The anatomy of three commodity booms," Resources Policy, Elsevier, vol. 31(1), pages 56-64, March.
    30. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    31. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    32. Mullineaux, Donald J, 1980. "Unemployment, Industrial Production, and Inflation Uncertainty in the United States," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 163-169, May.
    33. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    34. Lutz Kilian & Bruce Hicks, 2013. "Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 385-394, August.
    35. 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.
    36. James Rossiter, 2010. "Nowcasting the Global Economy," Discussion Papers 10-12, Bank of Canada.
    37. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    38. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    39. Kim, Soyoung & Roubini, Nouriel, 2000. "Exchange rate anomalies in the industrial countries: A solution with a structural VAR approach," Journal of Monetary Economics, Elsevier, vol. 45(3), pages 561-586, June.
    40. Angus Deaton, 2005. "ERRATUM: Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 395-395, May.
    41. Miron, Jeffrey A. & Romer, Christina D., 1990. "A New Monthly Index of Industrial Production, 1884–1940," The Journal of Economic History, Cambridge University Press, vol. 50(2), pages 321-337, June.
    42. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
    43. Baumeister, Christiane & Hamilton, James, 2017. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Deman," CEPR Discussion Papers 12532, C.E.P.R. Discussion Papers.
    44. Herrera, Ana María & Lagalo, Latika Gupta & Wada, Tatsuma, 2011. "Oil Price Shocks And Industrial Production: Is The Relationship Linear?," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 472-497, November.
    45. Frederick R. Macaulay, 1938. "Some Theoretical Problems Suggested by the Movements of Interest Rates, Bond Yields and Stock Prices in the United States since 1856," NBER Books, National Bureau of Economic Research, Inc, number maca38-1.
    46. Kim, Soyoung, 2001. "International transmission of U.S. monetary policy shocks: Evidence from VAR's," Journal of Monetary Economics, Elsevier, vol. 48(2), pages 339-372, October.
    47. Gerlach, H M Stefan, 1988. "World Business Cycles under Fixed and Flexible Exchange Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 20(4), pages 621-632, November.
    48. Charles W. Calomiris & R. Glenn Hubbard, 1989. "Price Flexibility, Credit Availability, and Economic Fluctuations: Evidence from the United States, 1894–1909," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 104(3), pages 429-452.
    49. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    50. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Why are crude oil prices high when global activity is weak?," Economics Letters, Elsevier, vol. 121(1), pages 133-136.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Ravazzolo & Joaquin L. Vespignani, 2015. "A new monthly indicator of global real economic activity," Globalization Institute Working Papers 244, Federal Reserve Bank of Dallas.
    2. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    4. Lorusso, Marco & Pieroni, Luca, 2018. "Causes and consequences of oil price shocks on the UK economy," Economic Modelling, Elsevier, vol. 72(C), pages 223-236.
    5. Ratti, Ronald A. & Vespignani, Joaquin L., 2015. "Commodity prices and BRIC and G3 liquidity: A SFAVEC approach," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 18-33.
    6. Dong, Minyi & Chang, Chun-Ping & Gong, Qiang & Chu, Yin, 2019. "Revisiting global economic activity and crude oil prices: A wavelet analysis," Economic Modelling, Elsevier, vol. 78(C), pages 134-149.
    7. 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.
    8. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    9. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    10. Raghavan, Mala, 2020. "An analysis of the global oil market using SVARMA models," Energy Economics, Elsevier, vol. 86(C).
    11. Lutz Kilian & Xiaoqing Zhou, 2023. "The Econometrics of Oil Market VAR Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 65-95, Emerald Group Publishing Limited.
    12. 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.
    13. 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).
    14. Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "Oil prices and global factor macroeconomic variables," Energy Economics, Elsevier, vol. 59(C), pages 198-212.
    15. Raghavan, Mala, 2015. "The macroeconomic effects of oil price shocks on ASEAN-5 economies," Working Papers 2015-10, University of Tasmania, Tasmanian School of Business and Economics.
    16. Christiane Baumeister & Lutz Kilian, 2016. "Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 139-160, Winter.
    17. Francesco Ravazzolo & Philip Rothman, 2013. "Oil and U.S. GDP: A Real-Time Out-of-Sample Examination," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(2-3), pages 449-463, March.
    18. Ratti, Ronald A. & Vespignani, Joaquin L., 2014. "Oil prices and the economy: A global perspective," MPRA Paper 59407, University Library of Munich, Germany.
    19. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    20. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:een:camaaa:2017-42. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Cama Admin (email available below). General contact details of provider: https://edirc.repec.org/data/asanuau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.