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Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time

Author

Listed:
  • Mr. Serkan Arslanalp
  • Mr. Marco Marini
  • Ms. Patrizia Tumbarello

Abstract

Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by comparing them with official statistics on trade and maritime statistics. If the challenges associated with port call data are overcome through appropriate filtering techniques, we show that these emerging “big data” on vessel traffic could allow statistical agencies to complement existing data sources on trade and introduce new statistics that are more timely (real time), offering an innovative way to measure trade activity. That, in turn, could facilitate faster detection of turning points in economic activity. The approach could be extended to create a real-time worldwide indicator of global trade activity.

Suggested Citation

  • Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019. "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers 2019/275, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2019/275
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    References listed on IDEAS

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    Citations

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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. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    3. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
    4. Kei Kanamoto & Liwen Murong & Minato Nakashima & Ryuichi Shibasaki, 2021. "Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 211-236, June.
    5. 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.
    6. 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).
    7. Chen, Hongyi & Tillmann, Peter, 2023. "Lockdown spillovers," Journal of International Money and Finance, Elsevier, vol. 137(C).
    8. 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).
    9. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.

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