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

Vincent Stamer

No 2179, Kiel Working Papers from Kiel Institute for the World Economy (IfW Kiel)

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.

Keywords: Trade; Forecasting; Machine Learning; Container Shipping (search for similar items in EconPapers)
JEL-codes: C53 F17 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-int
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/229168/1/1746394021.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwkwp:2179

Access Statistics for this paper

More papers in Kiel Working Papers from Kiel Institute for the World Economy (IfW Kiel) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().