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Thinking outside the container: A machine learning approach to forecasting trade flows

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  • Stamer, Vincent

Abstract

Global container ship movements may reliably predict global trade flows. Aggregating both movements at sea and port call events produces a wealth of explanatory variables. The machine learning algorithm partial least squares can map these explanatory time series to unilateral imports and exports, as well as bilateral trade flows. Applying out-of-sample and time series methods on monthly trade data of 75 countries, this paper shows that the new shipping indicator outperforms benchmark models for the vast majority of countries. This holds true for predictions for the current and subsequent month even if one limits the analysis to data during the first half of the month. This makes the indicator available at least as early as other leading indicators.

Suggested Citation

  • Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
  • Handle: RePEc:zbw:ifwkwp:2179
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    References listed on IDEAS

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    5. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    6. 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.
    7. Mr. Diego A. Cerdeiro & Andras Komaromi & Yang Liu & Mamoon Saeed, 2020. "World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch," IMF Working Papers 2020/057, International Monetary Fund.
    8. Christian Grimme & Klaus Wohlrabe, 2014. "Ifo Export Expectations – a New Indicator on the Export Industry in Germany," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(23), pages 64-65, December.
    9. Theodore Papageorgiou & Myrto Kalouptsidi & Giulia Brancaccio, 2017. "Geography, Search Frictions and Trade Costs," 2017 Meeting Papers 1105, Society for Economic Dynamics.
    10. Giulia Brancaccio & Myrto Kalouptsidi & Theodore Papageorgiou, 2020. "Geography, Transportation, and Endogenous Trade Costs," Econometrica, Econometric Society, vol. 88(2), pages 657-691, March.
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    Cited by:

    1. Saskia Meuchelböck & Vincent Stamer, 2021. "Hochfrequenzdaten aus der Schifffahrt als Indikator für den deutschen Außenhandel [Economic headlights: High-frequency data from shipping as an indicator of German foreign trade]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 101(5), pages 403-404, May.
    2. Jan Wedemeier & Lukas Wolf, 2022. "Navigating Rough Waters: Global Shipping and Challenges for the North Range Ports," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(3), pages 192-198, May.
    3. Alexander Sandkamp & Vincent Stamer & Shuyao Yang, 2022. "Where has the rum gone? The impact of maritime piracy on trade and transport," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 751-778, August.
    4. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Jannsen, Nils & Kooths, Stefan & Meuchelböck, Saskia, 2021. "Deutsche Wirtschaft im Frühjahr 2021 - Erholung vor zweitem Anlauf [German Economy Spring 2021 - Recovery ready for second take off]," Kieler Konjunkturberichte 77, Kiel Institute for the World Economy (IfW Kiel).
    5. 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.
    6. Peter Buchholz & Arne Schumacher & Siyamend Barazi, 2022. "Big data analyses for real-time tracking of risks in the mineral raw material markets: implications for improved supply chain risk management," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 701-744, December.

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    More about this item

    Keywords

    Trade; Forecasting; Machine Learning; Container Shipping;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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