Thinking Outside the Container: A Machine Learning Approach to Forecasting Trade Flows — Vincent Stamer

Sprecher

Vincent Stamer (Kiel Institute; Kiel University (CAU))

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.

here the link to the paper

Autoren

Vincent Stamer (Kiel Institute, Kiel University (CAU))

Raum

Virtuell über Gotomeeting
Bei Interesse senden Sie bitte eine E-Mail an frank.bickenbach@ifw-kiel.de um einen Gotomeeting-Link zum Seminar zu erhalten.