Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229168
Authors: 
Stamer, Vincent
Year of Publication: 
2021
Series/Report no.: 
Kiel Working Paper No. 2179
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.
Subjects: 
Trade
Forecasting
Machine Learning
Container Shipping
JEL: 
F17
C53
Document Type: 
Working Paper

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