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A Dynamic Factor Model for World Trade Growth

Author

Listed:
  • Stéphanie Guichard

    (OECD)

  • Elena Rusticelli

    (OECD)

Abstract

This paper reviews the main monthly indicators that could help forecasting world trade and compares different type of forecasting models using these indicators. In particular it develops dynamic factor models (DFM) which have the advantage of handling larger datasets of information than bridge models and allowing for the inclusion of numerous monthly indicators on a national and world-wide level such as financial indicators, transportation and shipping indices, supply and orders variables and information technology indices. The comparison of the forecasting performance of the DFMs with more traditional bridge equation models as well as autoregressive benchmarking models shows that, the dynamic factor approach seems to perform better, especially when a large set of indicators is used, but also that the marginal gains in adding indicators seems to diminish after a certain stage. Un modèle à facteurs dynamiques pour prévoir la croissance du commerce mondial Ce document passe en revue les principaux indicateurs mensuels pouvant aider á prévoir le commerce mondial et compare différents types de modèles de prévision utilisant ces indicateurs. En particulier, il développe des modèles á facteurs dynamiques (DFM) qui ont l'avantage de permettre l’utilisation de plus de séries que les modèles d’étalonnage et donc d’inclure des indicateurs mensuels au niveau national et mondial tels que les indicateurs financiers, de transport et d’expédition, d’approvisionnement et de carnets de commandes ou encore et de technologie de l’information. La comparaison de la performance de prévision des DFM avec des modèles d’étalonnage plus traditionnels ou des modèles autoregressifs montre que l'approche en facteurs dynamiques semble plus performante, surtout quand un vaste ensemble d'indicateurs est utilisé ; les gains marginaux en ajoutant des indicateurs semblent toutefois diminuer après un certain stade.

Suggested Citation

  • Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
  • Handle: RePEc:oec:ecoaaa:874-en
    DOI: 10.1787/5kg9zbvvwqq2-en
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    References listed on IDEAS

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    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. 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.
    3. 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.
    4. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    5. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    6. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    7. Barhoumi, Karim & Darné, Olivier & Ferrara, Laurent, 2016. "A World Trade Leading Index (WTLI)," Economics Letters, Elsevier, vol. 146(C), pages 111-115.
    8. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    9. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
    10. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    11. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    12. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    13. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    14. Myriam Morin & Cyrille Schwellnus, 2014. "An Update of the OECD International Trade Equations," OECD Economics Department Working Papers 1129, OECD Publishing.

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    More about this item

    Keywords

    bridge models; Commerce mondial; dynamic factor models; forecasting; modèle d’étalonnage; modèles á facteurs dynamiques; prévisions; world trade;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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