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|z 9781513544106
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Cerdeiro, Diego.
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|a World Seaborne Trade in Real Time :
|b A Proof of Concept for Building AIS-based Nowcasts from Scratch /
|c Diego Cerdeiro, Andras Komaromi, Yang Liu, Mamoon Saeed.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2020.
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|a 1 online resource (44 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a Maritime data from the Automatic Identification System (AIS) have emerged as a potential source for real time information on trade activity. However, no globally applicable end-to-end solution has been published to transform raw AIS messages into economically meaningful, policy-relevant indicators of international trade. Our paper proposes and tests a set of algorithms to fill this gap. We build indicators of world seaborne trade using raw data from the radio signals that the global vessel fleet emits for navigational safety purposes. We leverage different machine-learning techniques to identify port boundaries, construct port-to-port voyages, and estimate trade volumes at the world, bilateral and within-country levels. Our methodology achieves a good fit with official trade statistics for many countries and for the world in aggregate. We also show the usefulness of our approach for sectoral analyses of crude oil trade, and for event studies such as Hurricane Maria and the effect of measures taken to contain the spread of the novel coronavirus. Going forward, ongoing refinements of our algorithms, additional data on vessel characteristics, and country-specific knowledge should help improve the performance of our general approach for several country cases.
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|a Mode of access: Internet
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|a Komaromi, Andras.
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|a Liu, Yang.
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|a Saeed, Mamoon.
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|a IMF Working Papers; Working Paper ;
|v No. 2020/057
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2020/057/001.2020.issue-057-en.xml
|z IMF e-Library
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