  The World Trade Web (WTW) is the network of international trade relationships
among world countries. Characterizing both the local link weights (observed
trade volumes) and the global network structure (large-scale topology) of the
WTW via a single model is still an open issue. While the traditional Gravity
Model (GM) successfully replicates the observed trade volumes by employing
macroeconomic properties such as GDP and geographic distance, it,
unfortunately, predicts a fully connected network, thus returning a completely
unrealistic topology of the WTW. To overcome this problem, two different
classes of models have been introduced in econometrics and statistical physics.
Econometric approaches interpret the traditional GM as the expected value of a
probability distribution that can be chosen arbitrarily and tested against
alternative distributions. Statistical physics approaches construct
maximum-entropy probability distributions of (weighted) graphs from a chosen
set of measurable structural constraints and test distributions resulting from
different constraints. Here we compare and integrate the two approaches by
considering a class of maximum-entropy models that can incorporate
macroeconomic properties used in standard econometric models. We find that the
integrated approach achieves a better performance than the purely econometric
one. These results suggest that the maximum-entropy construction can serve as a
viable econometric framework wherein extensive and intensive margins can be
separately controlled for, by combining topological constraints and dyadic
macroeconomic variables.

Physics and Society;Econometrics;Data Analysis, Statistics and Probability