Arbeitspapier
Thinking outside the container: A machine learning approach to forecasting trade flows
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.
- Language
-
Englisch
- Bibliographic citation
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Series: Kiel Working Paper ; No. 2179
- Classification
-
Wirtschaft
Trade: Forecasting and Simulation
Forecasting Models; Simulation Methods
- Subject
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Trade
Forecasting
Machine Learning
Container Shipping
- Event
-
Geistige Schöpfung
- (who)
-
Stamer, Vincent
- Event
-
Veröffentlichung
- (who)
-
Kiel Institute for the World Economy (IfW)
- (where)
-
Kiel
- (when)
-
2021
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Stamer, Vincent
- Kiel Institute for the World Economy (IfW)
Time of origin
- 2021
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