Series: Working Papers. 2019.
Author: Jaime Martínez-Martín and Elena Rusticelli.
Topics: Quantitative methods | International trade | Crisis | Exchange rates | International Economy.
Published in: International Journal of Forecasting 
Full document
Abstract
This paper builds an innovative composite world trade cycle index (WTI) by means of
a dynamic factor model to perform short-term forecasts of world trade growth of both
goods and (usually neglected) services. The selection of trade indicator series is made
using a multidimensional approach, including Bayesian model averaging techniques,
dynamic correlations and Granger non-causality tests in a linear VAR framework. To
overcome the real-time forecasting challenges, the dynamic factor model is extended
to account for mixed frequencies, to deal with asynchronous data publication and to
include hard and survey data along with leading indicators. Nonlinearities are addressed
with a Markov switching model. In the empirical application, simulations analysis in
pseudo real-time suggest that: i) the global trade index is a very useful tool for tracking
and forecasting world trade in real time; ii) the model is able to infer global trade cycles
very precisely and better than several competing alternatives; and iii) global trade finance
conditions seem to lead the trade cycle, in line with the theoretical literature.