This paper reviews the main monthly indicators that could help forecasting world trade and compares
different type of forecasting models using these indicators. In particular it develops dynamic factor models
(DFM) which have the advantage of handling larger datasets of information than bridge models and
allowing for the inclusion of numerous monthly indicators on a national and world-wide level such as
financial indicators, transportation and shipping indices, supply and orders variables and information
technology indices. The comparison of the forecasting performance of the DFMs with more traditional
bridge equation models as well as autoregressive benchmarking models shows that, the dynamic factor
approach seems to perform better, especially when a large set of indicators is used, but also that the
marginal gains in adding indicators seems to diminish after a certain stage.
