Thinking Outside the Container: A Sparse Partial Least Squares 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.

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