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A World Trade Leading Index (WTLI)

Author

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  • Barhoumi, Karim
  • Darné, Olivier
  • Ferrara, Laurent

Abstract

This paper develops a new monthly World Trade Leading Indicator (WTLI) that relies on a dynamic factor model estimated on set of leading indicators of world trade activity. Compared to the CPB World Trade Monitor’s benchmark indicator for global trade the WTLI captures turning points in global trade with an average lead between 2 and 3 months. This new tool can provide policy makers with valuable foresight into the future direction of economic activity by tracking world trade more efficiently.

Suggested Citation

  • Barhoumi, Karim & Darné, Olivier & Ferrara, Laurent, 2016. "A World Trade Leading Index (WTLI)," Economics Letters, Elsevier, vol. 146(C), pages 111-115.
    • Handle: RePEc:eee:ecolet:v:146:y:2016:i:c:p:111-115
    DOI: 10.1016/j.econlet.2016.07.032
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    1. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    2. Cristina Constantinescu & Aaditya Mattoo & Michele Ruta, 2020. "The Global Trade Slowdown: Cyclical or Structural?," The World Bank Economic Review, World Bank, vol. 34(1), pages 121-142.
    3. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    4. Troy D. Matheson, 2014. "New indicators for tracking growth in real time," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 51-71.
    5. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
    6. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2013. "Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 64-79, February.
    7. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    9. Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
    10. repec:dau:papers:123456789/8088 is not listed on IDEAS
    11. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
    12. Matthieu Bussière & Giovanni Callegari & Fabio Ghironi & Giulia Sestieri & Norihiko Yamano, 2013. "Estimating Trade Elasticities: Demand Composition and the Trade Collapse of 2008-2009," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(3), pages 118-151, July.
    13. Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 193-225.
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    Cited by:

    1. Martínez-Martín, Jaime & Rusticelli, Elena, 2021. "Keeping track of global trade in real time," International Journal of Forecasting, Elsevier, vol. 37(1), pages 224-236.
    2. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    3. Menzie Chinn & Baptiste Meunier & Sebastian Stumpner, 2023. "Nowcasting world trade in real time with machine learning [Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]," Bulletin de la Banque de France, Banque de France, issue 248.
    4. Çiðdem Kurt Cihangir, 2018. "Küresel Risk Algýsýnýn Küresel Ticaret Üzerindeki Etkisi," Isletme ve Iktisat Calismalari Dergisi, Econjournals, vol. 6(1), pages 1-10.

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    More about this item

    Keywords

    World trade; Leading indicators; Factor models;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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